{ "paper_id": "O14-5000", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T08:04:33.852462Z" }, "title": "Computational Linguistics & Chinese Language Processing Aims and Scope", "authors": [ { "first": "Jen-Tzung", "middle": [], "last": "Chien", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Hung-Yu", "middle": [], "last": "Kao", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Chia-Hui", "middle": [], "last": "Chang", "suffix": "", "affiliation": {}, "email": "cheng@nlplab.cc" }, { "first": "Yi-An", "middle": [], "last": "Wu", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Shu-Kai", "middle": [], "last": "Hsieh", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Chao-Yu", "middle": [], "last": "Su", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Chiu-Yu", "middle": [], "last": "Tseng", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Jyh-Shing", "middle": [ "Roger" ], "last": "Jang", "suffix": "", "affiliation": { "laboratory": "", "institution": "National Taiwan University", "location": { "country": "Taiwan" } }, "email": "jang@mirlab.org" }, { "first": "-Mei", "middle": [], "last": "Li", "suffix": "", "affiliation": {}, "email": "" }, { "first": "", "middle": [], "last": "Chen", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Yu-Yang", "middle": [], "last": "Lin", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Chang", "middle": [], "last": "Chia-Hui\uf02a", "suffix": "", "affiliation": {}, "email": "" }, { "first": "#", "middle": [], "last": "Sites", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Shu-Kai", "middle": [], "last": "Hsieh\uf02a", "suffix": "", "affiliation": {}, "email": "" }, { "first": "\u9ec3\u51a0\u8aa0", "middle": [ "\uf02a" ], "last": "\u3001\u5433\u9451\u57ce", "suffix": "", "affiliation": {}, "email": "" }, { "first": "\uf02a", "middle": [], "last": "\u3001\u8a31\u6e58\u7fce", "suffix": "", "affiliation": {}, "email": "" }, { "first": "\uf02a", "middle": [], "last": "\u3001\u984f\u5b5c\u66e6", "suffix": "", "affiliation": {}, "email": "" }, { "first": "\uf02a", "middle": [], "last": "\u3001\u5f35\u4fca\u76db", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Guan-Cheng", "middle": [], "last": "Huang", "suffix": "", "affiliation": {}, "email": "hsiang@nlplab.cc" }, { "first": "Jian-Cheng", "middle": [], "last": "Wu", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Hsiang-Ling", "middle": [], "last": "Hsu", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Tzu-Hsi", "middle": [], "last": "Yen", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Jason", "middle": [ "S" ], "last": "Chang", "suffix": "", "affiliation": {}, "email": "jschang@cs.nthu.edu.tw" }, { "first": "Po-Han", "middle": [], "last": "Hao", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Ssu-Cheng", "middle": [], "last": "Chen\uf02a", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Berlin", "middle": [], "last": "Chen\uf02a", "suffix": "", "affiliation": {}, "email": "berlin@csie.ntnu.edu.tw" }, { "first": "Ssu-Cheng", "middle": [], "last": "Chen", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Berlin", "middle": [], "last": "Chen", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Chao-Yu", "middle": [ "Su" ], "last": "\uf02a\uf02b", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Chiu-Yu", "middle": [], "last": "Tseng\uf02b", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Li-Mei", "middle": [], "last": "Chen", "suffix": "", "affiliation": {}, "email": "" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "ROCLING, which sponsored by the Association for Computational Linguistics and Chinese Language Processing (ACLCLP), is the leading and most comprehensive conference on computational linguistics and speech processing in Taiwan, bringing together researchers, scientists and industry participants from fields of computational linguistics, information understanding, and speech processing, to present their work and discuss recent trends in the field. This special issue presents extended and reviewed versions of five papers meticulously selected from ROCLING 2014, including 3 natural language processing papers and 2 speech processing papers. The first paper from National Central University focused on constructing a large POI (Point-of Interest) database. They solve problems of Taiwan address normalization, store name extraction, and the matching of addresses and store names by training a statistical model. They obtain 0.791 F-measure for store name recognition on search snippets. The second paper from National Taiwan University extracted policy positions from data collected from recent highly-debated Cross-Strait Service Trade Agreement (CSSTA) to predict the electoral behavior from this information. They used the keywords of each position to do the binary classification of the texts and count the score of how positive or negative attitudes toward CSSTA. The proposed approach saves human labor of the traditional content analysis and increases the objectivity of the judgment standard. The third paper from National Tsing Hua University constructed a system to aid academia paper writing. They used writing common patterns to train a writing sentence classifier. This system can also provide hints for users to guild their paper writing. The last two papers are speech processing papers. The paper from National Taiwan Normal University investigated and developed language model adaptation techniques for use in ASR (automatic speech recognition). The proposed approach measured the relationships between a search history and an upcoming word. Their language models can offer substantial improvements over the baseline N-gram system and some state-of-the-art language model adaptation methods. The paper from Academia Sinica, Taiwan presented study examines prosodic characteristics of Taiwan (TW) English in relation to native (L1) English and TW speakers' mother tongue, Mandarin. By examining prosodic patterns of word/sentence, similarity analysis in this paper suggests that between-speaker similarity is greater when they are in the same speaker group in both word and sentence layer.", "pdf_parse": { "paper_id": "O14-5000", "_pdf_hash": "", "abstract": [ { "text": "ROCLING, which sponsored by the Association for Computational Linguistics and Chinese Language Processing (ACLCLP), is the leading and most comprehensive conference on computational linguistics and speech processing in Taiwan, bringing together researchers, scientists and industry participants from fields of computational linguistics, information understanding, and speech processing, to present their work and discuss recent trends in the field. This special issue presents extended and reviewed versions of five papers meticulously selected from ROCLING 2014, including 3 natural language processing papers and 2 speech processing papers. The first paper from National Central University focused on constructing a large POI (Point-of Interest) database. They solve problems of Taiwan address normalization, store name extraction, and the matching of addresses and store names by training a statistical model. They obtain 0.791 F-measure for store name recognition on search snippets. The second paper from National Taiwan University extracted policy positions from data collected from recent highly-debated Cross-Strait Service Trade Agreement (CSSTA) to predict the electoral behavior from this information. They used the keywords of each position to do the binary classification of the texts and count the score of how positive or negative attitudes toward CSSTA. The proposed approach saves human labor of the traditional content analysis and increases the objectivity of the judgment standard. The third paper from National Tsing Hua University constructed a system to aid academia paper writing. They used writing common patterns to train a writing sentence classifier. This system can also provide hints for users to guild their paper writing. The last two papers are speech processing papers. The paper from National Taiwan Normal University investigated and developed language model adaptation techniques for use in ASR (automatic speech recognition). The proposed approach measured the relationships between a search history and an upcoming word. Their language models can offer substantial improvements over the baseline N-gram system and some state-of-the-art language model adaptation methods. The paper from Academia Sinica, Taiwan presented study examines prosodic characteristics of Taiwan (TW) English in relation to native (L1) English and TW speakers' mother tongue, Mandarin. By examining prosodic patterns of word/sentence, similarity analysis in this paper suggests that between-speaker similarity is greater when they are in the same speaker group in both word and sentence layer.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Abstract", "sec_num": null } ], "body_text": [ { "text": "Chuang, H.-M., Chang, C.-H., & Kao, T.-Y. (2014) . Effective Web Crawling for Chinese Addresses and Associated Information. in EC-Web, Munich, Germany, 2014.", "cite_spans": [ { "start": 15, "end": 48, "text": "Chang, C.-H., & Kao, T.-Y. (2014)", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "Deriving reliable estimates of public opinions is central to the study of electoral behavior and policy positions. Among different methods, linguistic strategy has been one of the most widely used approaches in related studies in the field of political communication. For instance, budge et al. (1987) utilizes discourse-level opinion interpretation and stance recognition; while laver et al. (2003) and klemmensen et al. (2007) treated the words as \"data\"' encoding information about the political position of the texts' author. In addition to theoretical surveys, there are also numerous appealing applications on the political positions such as abgeordnetenwatch 1 , where citizens are able to ask the members of parliament questions and express their attitudes through surveys, and the members of parliaments also respond to the questions. The dynamic design often attracts large organizations and political parties to keep a close eye on how the public form and represent their political stance, thus enhancing the \uf02a Graduate Institute of Linguistics, National Taiwan University E-mail: transparency and accountability in the development of democracy.", "cite_spans": [ { "start": 268, "end": 301, "text": "For instance, budge et al. (1987)", "ref_id": null }, { "start": 393, "end": 428, "text": "(2003) and klemmensen et al. (2007)", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "Over the past few years, the production of huge volume of textual data has become an essential part of our current social life. In this context, there have been growing interests in applying text mining techniques to support Natural Language Processing applications in social and political domains, ranging from subjectivity and opinion mining, to ontologies and knowledge discovery. More and more attentions have been paid to the analysis and prediction tasks from the social media (Tumasjan et al., 2010; Conover et al., 2011; Bermingham & Smeaton, 2011) , which set a new scene for the data-driven research paradigm for social and political domains.", "cite_spans": [ { "start": 483, "end": 506, "text": "(Tumasjan et al., 2010;", "ref_id": "BIBREF12" }, { "start": 507, "end": 528, "text": "Conover et al., 2011;", "ref_id": "BIBREF3" }, { "start": 529, "end": 556, "text": "Bermingham & Smeaton, 2011)", "ref_id": "BIBREF1" } ], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "Recently, the public of Taiwan has had a heated debate on the issue of Cross-Strait Service Trade Agreement (CSSTA). After months of simmering tensions between ruling party and opposition party strongly backed by the student-led Sunflower Movement, the debate has finally reached a breaking point on March 18, 2014, at which students occupied the Legislative Yuan. This action of \"Occupy Taiwan Legislature\" marked the beginning of a series of different political negotiations and efforts on this topic for both sides till April, 7. During this period, as well-recognized by many, using of novel communication technologies -Facebook sharing, instant messaging, sparking discussions on PTT, cloud documentation, etchave reshaped the social movement not only domestically, but also globally. 2 The uncertainty among members of society over the implementation of CSSTA is palpable. Due to its nature of easy access and instant response, the social media has become the dominant source in opinion shaping and the accompanying sentiment spread. The extraction and tracking of uprising political opinions and events has thus become one of the most important topics that must be now be reckoned with. Though the task of analyzing and interpreting the social and political texts has gained its popularity in NLP-aided Social Science related fields, with the huge amounts of texts, it is not possible to analyze them manually. Instead, we propose to use the text mining approach, which automatically extract opinion and information profiles from the texts. In addition, this approach also strengthens the objectivity, for the norms are set a priori, thus human bias is reduced.", "cite_spans": [ { "start": 790, "end": 791, "text": "2", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "Our work is motivated by the compelling study of Junqu\u00e9 de Fortuny et al. (2012) which analyzed political opinions in Belgium by text mining of the newspapers. They used sentiment analysis to detect the opinion of the texts, and found the trends over timeline. Gelbukh et al. (1999) also used text mining techniques to analyze the Internet and newspaper news. They extracted the information of the texts by three steps: finding the topic of the document, 2 Interested readers can refer to the cloud folder at http://hackfoldr.org/congressoccupied/ and the popular forum at http://www.reddit.com/r/IAmA/comments/21xsaz/we_are_students_that_have_taken_over_taiwans Public Opinion Toward CSSTA: A Text Mining Approach 21 extracting the opinion paragraphs by pattern matching, and matching topics with opinion paragraphs. They intended to discover how society interests are changing and to identify important current topics of opinion.", "cite_spans": [ { "start": 261, "end": 282, "text": "Gelbukh et al. (1999)", "ref_id": "BIBREF4" }, { "start": 455, "end": 456, "text": "2", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "As a pioneering work in the context of Taiwan society, this research aims to trace the public opinion toward CSSTA from the perspective of text mining. The approach involves the manually extracting of political stance related keywords and phrases, supervised machining learning, and a statistical model of the trend. We focus on the individual posts on PTT rather than news since they are more representative. The potential political or commercial applications are valuable. One can discover the public opinion and response in a short time. This paper is organized as follows: first, we introduce some backgrounds of the studies of policy positions in section 2. Our approach to this topic and also the materials we used is described in section 3. The validity of our approach and the results are shown in section 4. Section 5 concludes the paper and suggests future works.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "There is a growing body of studies on the topic of analysis policy positions. One traditional approach is content analysis, such as the Comparative Manifestos Project (CMP) (Budge et al., 1987; Benoit & Laver, 2007; Slapin & Proksch, 2008) , where thousands of manifestos over 50 countries are interpreted by human decoders. However, this approach is so costly that it requires a huge amount of human labor. Another approach is computerized coding schemes (Kleinnijenhuis & Pennings, 2001) , which match the texts to coding dictionaries. Laver and Garry (2000) created a dictionary of policy position which contains the predefined categories of political issues and the corresponding words. However, the approach also require much human labor on building dictionaries, and the words are insensitive to the contexts.", "cite_spans": [ { "start": 173, "end": 193, "text": "(Budge et al., 1987;", "ref_id": "BIBREF2" }, { "start": 194, "end": 215, "text": "Benoit & Laver, 2007;", "ref_id": "BIBREF0" }, { "start": 216, "end": 239, "text": "Slapin & Proksch, 2008)", "ref_id": "BIBREF11" }, { "start": 456, "end": 489, "text": "(Kleinnijenhuis & Pennings, 2001)", "ref_id": "BIBREF7" }, { "start": 538, "end": 560, "text": "Laver and Garry (2000)", "ref_id": "BIBREF9" } ], "ref_spans": [], "eq_spans": [], "section": "Previous Works", "sec_num": "2." }, { "text": "A variant of the second approach is the research of Laver et al. (2003) , where they compared words in two different types of texts. One is the reference texts whose policy positions are defined a priori, and the other is the virgin texts whose policy positions are unknown but need to be found out. This approach is similar to the conventional keyness calculation where the salient keywords in target texts are measured and weighted statistically in comparing with the reference texts. However, as mentioned in (Klemmensen et al., 2007) , the validity of the positions obtained by the this approach is \"dependent on the choice of reference text and the quality of the a priori scores attached to these reference texts.\" This poses a challenge for us because of the lack of representative reference corpus that can reflect the current language usage. 3 In this study, we adopt the second approach with a little variation, i.e. we also built the dictionary and tested its validity. More detailed procedures are explained ", "cite_spans": [ { "start": 52, "end": 71, "text": "Laver et al. (2003)", "ref_id": "BIBREF10" }, { "start": 512, "end": 537, "text": "(Klemmensen et al., 2007)", "ref_id": "BIBREF8" }, { "start": 851, "end": 852, "text": "3", "ref_id": "BIBREF95" } ], "ref_spans": [], "eq_spans": [], "section": "Previous Works", "sec_num": "2." }, { "text": "The material we used in this experiment includes a list of manually created seed words and phrases representing the pro-and-con political polarity, respectively. 8 linguistic graduate students from NTU were asked to compile the list based on their observations on the texts with CSSTA debate. It is noted that the keywords may be a word, a phrase, or a sentence. After some preprocessing, there are in total 350 terms for supporting CSSTA and also 350 terms for opposing CSSTA. We also use the texts on the website \"\u670d\u8cbf\u6771\u897f\u8ecd\" 4 to be our gold standards of supporting and opposing texts. The selected texts are used to do the evaluations of our keywords.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Materials", "sec_num": "3.1" }, { "text": "Another resource we used in this work is the PTT corpus, a social corpus which has been constructed and dynamically updated by LOPE lab at National Taiwan University 5 . As an online bulletin board favored by many of the youth, PPT is doubtless the largest public forum and social media in Taiwan, with more than 1.5 million registered users and over 150,000 users online during peak hours. Many newest information are posted instantly on the Gossiping board. We analyzed every post on Gossiping board from January 1, 2014 to July 1, 2014, in total around 150,000 posts.", "cite_spans": [ { "start": 166, "end": 167, "text": "5", "ref_id": "BIBREF97" } ], "ref_spans": [], "eq_spans": [], "section": "Materials", "sec_num": "3.1" }, { "text": "Basically, we follow the text mining techniques suggested by Gupta Gupta and Lehal (2009) , e.g. feature extraction, search and retrieval, categorization, and summarization. The detailed procedures are described as follows.", "cite_spans": [ { "start": 61, "end": 89, "text": "Gupta Gupta and Lehal (2009)", "ref_id": "BIBREF5" } ], "ref_spans": [], "eq_spans": [], "section": "Procedures", "sec_num": "3.2" }, { "text": "\uf0b7 Extract features.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Procedures", "sec_num": "3.2" }, { "text": "We arranged the works of every person with the unified format, which includes the keywords and the corresponding texts. Then we save the data in CSV files. \uf0b7 Open-sourced Chinese word segmentation with custom dictionary. In order to flexibly fit the target texts, we extend an open-sourced Chinese word segmentation system 6 . There are many long keywords in the texts, which needs to be reserved in segmentation, so we first create the user dictionary of every keyword and load it to Jieba before word segmentation. After segmentation, each text is saved as an document (a vector of features and weights). The weighting scheme of the model is TFIDF and the classifier is a SVM classifier, which separates the documents in a high-dimensional space by hyperplanes. \uf0b7 Use cross validation for evaluations. N-fold cross-validation performs N tests on a given classifier, each time partitioning the given dataset into different subsets for training and testing. The indices for evaluations are accuracy, precision, recall, F1, and standard deviation. \uf0b7 Calculate the information gain from the classification model. Information gain is a measure of a feature's predictability for a class label. Some features occur more frequently with definite type of texts, so they are more informative. The information gain is defined as", "cite_spans": [ { "start": 323, "end": 324, "text": "6", "ref_id": "BIBREF98" } ], "ref_spans": [], "eq_spans": [], "section": "Procedures", "sec_num": "3.2" }, { "text": "IG(T,a) = H(T) -H(T|a),", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Procedures", "sec_num": "3.2" }, { "text": "where H is the Information Entropy", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Procedures", "sec_num": "3.2" }, { "text": "2 ( ) ( )log ( ) i i i H X P X P X \uf03d \uf02d \uf0e5", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Procedures", "sec_num": "3.2" }, { "text": "The information gain is the entropy reduced by adding the new feature a. \uf0b7 Use the information gain to evaluate the texts from the PTT corpus.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Procedures", "sec_num": "3.2" }, { "text": "We search the keywords of every post. Each keyword has the weight of the information gain. We sum over the information gain to judge the stance of the post, and then the scores of every post are further summed up in a day in order to observe the daily trend.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Procedures", "sec_num": "3.2" }, { "text": "We choose the keywords as the first step since many terms can potentially reveal one's attitude. For instance, the supporter for CSSTA would call students \"\u9738\u4f54\", occupy, the parliament, while the opponent would use \"\u7559\u5b88\", stay, in the parliament. The supporter emphasized \"\u4fe1\u7528\", credibility, \"\u7d93\u6fdf\", economy, and \"\u79e9\u5e8f\", social order, while the opponent would stress the \"\u9ed1\u7bb1\", black box, \"\u884c\u52d5\", action, and \"\u6b63\u7fa9\", justice. The following are the word clouds for two types of keywords. It is worth noting here that although opinion mining and sentiment analysis are often considered synonymous in many studies, it is necessary to draw the line between these two concepts. Following (Xu & Li, 2013) , opinion is \"a statement of the personal position or beliefs regarding an event, an object, or a subject (opinion target), while sentiment is the author's emotional state that may be caused by an event, an object, or a subject (sentiment target)\". So as reflected in the lists of keywords, we may find words representing certain opinions may be associated with a sentiment (e.g., \"\u7834\u58de\", destroy), but there are cases with standalone opinions (e.g., \"\u958b\u653e\", open).", "cite_spans": [ { "start": 671, "end": 686, "text": "(Xu & Li, 2013)", "ref_id": "BIBREF13" } ], "ref_spans": [], "eq_spans": [], "section": "Keywords", "sec_num": "4.1" }, { "text": "We use these keywords as features to train the classifier. The gold standards of the texts are chosen from the \"\u670d\u8cbf\u6771\u897f\u8ecd\" website. The cross-validation yields the results in the table 1. There are about one third of keywords which can be found in our testing data. (supporting keywords: 116/350, opposing keywords: 136/350) The results show that 85 percent of the texts can be correctly classified as positive or negative opinion toward CSSTA by these keywords. Therefore, with the validity of our keywords selection, we are able to use the information gain of keywords to do the trend analysis.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Classifier", "sec_num": "4.2" }, { "text": "From the classification model, we also obtain the information gain of each keyword. The information gain means to what degree the keyword contains the political polarity. The larger the information gain of a word, the greater probability of distinguishing two types of texts by the word. Some samples are shown in table2. Some keywords can distinguish the texts better like \"\u7af6\u722d\", competition, and \"\u53cd\u670d\u8cbf\", anti-CSSTA, and thus they have more weights in classifying the texts. ", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Information Gain", "sec_num": "4.3" }, { "text": "While sentiments are always polar, it is not always the case for opinions. So instead of aiming to do binary classification of political texts only, we turn to use the information gain to do the trend analysis. First, we sum keywords of each post, and sum over the posts of the same day. In other words, the score of each date is calculated as the following equation:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Trend Analysis", "sec_num": "4.4" }, { "text": "Score ( ) ( ), post index, word i w IG w C w i w \uf03d \uf02a \uf03d \uf03d \uf0e5 \uf0e5", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Trend Analysis", "sec_num": "4.4" }, { "text": "where IG(w) denotes the information gain of a word w, C(w) denotes the word count of w, and the summation first sum over the word w in a post, then sum over the post i in a day. The reason why we sum up the values of IG's is that since IG is the change in information entropy, we can add up the entropy changes to see the tendencies of a text in the topic of CSSTA. Higher IG value means closer relations to the topic. The results are shown in the Figure 2 . The corresponding events are listed in the Table 3 . The figure demonstrates the popularity of this topic of each day, and the top spike remarkably indicates that the discussion on CSSTA increases abruptly from March 18, which was the date that protesters occupied Taiwan Legislative chamber, to the March 23, which was the date that some protesters further occupied the Executive Yuan. The Figure 3 shows the ratio of supporting CSSTA from the analysis of posts. We calculate the supporting information gain over the total information gain, and also sum over the posts in one day. We can add the information gains like the previous analysis since the IG's are entropy changes. The information gains are added in both supporting and opposing aspects, and are compared to show the polarity of a text. The figure shows that the trend of supporting rate of CSSTA. The supporting rate drops on March 19, because of the Sunflower student movement. The supporting rate fluctuates for two possible reasons: the quantity of posts differs every day, and also the content of posts varies drastically. Thus the scores of the keywords varies in a wide range, which lead to the fluctuation of the supporting rate. But in Public Opinion Toward CSSTA: A Text Mining Approach 27 general, we can see the tendency of the change. This method can be implemented on the coming election. The dynamic process of supporting rate for each candidate can be revealed by the texts on the social web, which is more efficient that the traditional telephone survey. Moreover, we can do more fine-grained analysis since the data is producing every day, and the We can ask, for example, how the event or the speech of the candidates affect their supporting rate. There are huge potential of the political interests.", "cite_spans": [], "ref_spans": [ { "start": 448, "end": 456, "text": "Figure 2", "ref_id": "FIGREF5" }, { "start": 502, "end": 509, "text": "Table 3", "ref_id": "TABREF3" }, { "start": 850, "end": 858, "text": "Figure 3", "ref_id": "FIGREF6" } ], "eq_spans": [], "section": "Trend Analysis", "sec_num": "4.4" }, { "text": "Mining and tracking political opinions from texts in the social media is a young yet important research area with both scientific significance and social impact. The goal of this paper is to move one step forward in this area in Chinese context. We started from the manually created keywords and key phrases of CSSTA, used them to build a classifier and calculated their information gain, and then did the trend analysis of the PTT corpus. This approach involves interdisciplinary fields including information retrieval, data mining, statistics, machine learning, and computational linguistics. We hope that this text mining approach could discover the public opinion toward CSSTA, and further reveal political stances. Future works include more sophisticated language processing techniques applied to more broad domain of political topics, as well as developing dynamic tracking system gearing up for year-end election 2014.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Conclusion", "sec_num": "5." }, { "text": "method for learning to classify the moves of a given set sentences in a academic paper. In our approach, we learn a set of move-specific common patterns, which are characteristic of moves, to help annotate sentences with moves. The method involves using statistical method to find common patterns in a corpus of research papers, assigning the patterns with moves, using patterns to annotate sentences in a corpus, and train a move classifier on the annotated sentences. At run-time, sentences are transformed into feature vectors to predict the given sentences. We present a prototype system, MoveTagger, that applies the method to a corpus of research papers. The proposed method outperforms previous research with a significantly higher accuracy. (\u4f8b\u5982\uff0cCooper, 1985; Hopkins, 1985; Crookes, 1986; Samraj, 2002 Samraj, , 2005 Salager-Meyer, 1990 , 1991 , 1992 ", "cite_spans": [ { "start": 749, "end": 766, "text": "(\u4f8b\u5982\uff0cCooper, 1985;", "ref_id": null }, { "start": 767, "end": 781, "text": "Hopkins, 1985;", "ref_id": null }, { "start": 782, "end": 796, "text": "Crookes, 1986;", "ref_id": null }, { "start": 797, "end": 809, "text": "Samraj, 2002", "ref_id": null }, { "start": 810, "end": 824, "text": "Samraj, , 2005", "ref_id": null }, { "start": 825, "end": 844, "text": "Salager-Meyer, 1990", "ref_id": null }, { "start": 845, "end": 851, "text": ", 1991", "ref_id": null }, { "start": 852, "end": 858, "text": ", 1992", "ref_id": "BIBREF29" } ], "ref_spans": [], "eq_spans": [], "section": "\u9ec3\u51a0\u8aa0 \u7b49", "sec_num": null }, { "text": "\u3002\u4e5f\u6709\u5b78\u8005\u6cbf\u7528 CARS \u6a21\u5f0f\u4f86\u5206 \u6790\u300c\u7d50\u679c\u300d \u7bc0(Thompson, 1993) 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\u662f\u6700\u5e38\u88ab\u4f7f\u7528\u7684\u4e3b\u984c\u6a21\u578b\u5be6\u4f8b\u3002\u5728\u6b64\u8209 (Dempster, 1977) (Kullback & Leibler, 1951; Zhai, 2008) (Blei, 2014; Chen et al., 2004; Kim et al., 2013; Zhai, 2008) \u61c9\u7528\u5230 \u4e2d\u6587\u5927\u8a5e\u5f59\u9023\u7e8c\u8a9e\u97f3\u8fa8\u8b58\u7684\u4efb\u52d9\u4e0a\u3002 ", "cite_spans": [ { "start": 7, "end": 29, "text": "(Blei & Lafferty, 2009", "ref_id": "BIBREF16" }, { "start": 109, "end": 124, "text": "(Hofmann, 1999)", "ref_id": "BIBREF24" }, { "start": 172, "end": 191, "text": "(Blei et al., 2003)", "ref_id": "BIBREF17" }, { "start": 192, "end": 204, "text": "(Kuhn, 1988)", "ref_id": "BIBREF26" }, { "start": 205, "end": 220, "text": "(Hofmann, 1999)", "ref_id": "BIBREF24" }, { "start": 268, "end": 287, "text": "(Blei et al., 2003)", "ref_id": "BIBREF17" }, { "start": 306, "end": 322, "text": "(Dempster, 1977)", "ref_id": "BIBREF20" }, { "start": 323, "end": 349, "text": "(Kullback & Leibler, 1951;", "ref_id": "BIBREF27" }, { "start": 350, "end": 361, "text": "Zhai, 2008)", "ref_id": null }, { "start": 362, "end": 374, "text": "(Blei, 2014;", "ref_id": "BIBREF15" }, { "start": 375, "end": 393, "text": "Chen et al., 2004;", "ref_id": "BIBREF18" }, { "start": 394, "end": 411, "text": "Kim et al., 2013;", "ref_id": "BIBREF25" }, { "start": 412, "end": 423, "text": "Zhai, 2008)", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "\u9ec3\u51a0\u8aa0 \u7b49", "sec_num": null }, { "text": "\uff0c\u7528\u5728\u8a9e\u97f3\u8fa8\u8b58\u904e\u7a0b\u4e2d \u52d5\u614b\u4f86\u8f14\u52a9\u6216\u8abf\u6574 N \u9023\u8a9e\u8a00\u6a21\u578b\u65bc\u9810\u6e2c\u8a5e\u5f59\u51fa\u73fe\u7684\u6a5f\u7387\u3002\u5176\u57fa\u672c\u6982\u5ff5\u662f\u5982\u679c\u6211\u5011\u8b1b\u4e86\u4e00 \u4e9b\u8a5e\u5f59\uff0c\u5247\u4e00\u6bb5\u6642\u9593\u5167\u9019\u4e9b\u8a5e\u5f59\u518d\u6b21\u51fa\u73fe\u7684\u6a5f\u7387\u6703\u5f88\u9ad8\u3002\u6211\u5011\u56e0\u6b64\u53ef\u4ee5\u5229\u7528\u6b64\u7dda\u7d22\u5728\u8a9e \u97f3\u8fa8\u8b58\u904e\u7a0b\u4e2d\u4e0d\u65b7\u5730\u7522\u751f\u4e00\u500b\u8a9e\u8a00\u6a21\u578b(\u4f8b\u5982\u55ae\u9023\u5feb\u53d6\u6a21\u578b)\uff0c\u4e26\u900f\u904e\u7dda\u6027\u7d44\u5408\u7684\u65b9\u5f0f\u8207 \u539f\u59cb N \u9023\u8a9e\u8a00\u6a21\u578b(\u4f8b\u5982\u4e09\u9023\u8a9e\u8a00\u6a21\u578b)\u7d50\u5408\u4f86\u52d5\u614b\u5730\u8abf\u9069\u8a9e\u97f3\u8fa8\u8b58\u6240\u9700\u7684\u8a9e\u8a00\u6a21\u578b\uff1a \uf028 \uf029 i i i i i i i i i H H w n w w w P w w w P , ) 1 ( ) | ( ) | ( 1 2 Trigram 1 2 Trigram \uf0d7 \uf02d \uf02b \uf0d7 \uf03d \uf02d \uf02d \uf02d \uf02d \uf06c \uf06c (1) \u5176\u4e2d i H \u4ee3\u8868\u8a5e\u5f59 i w \u5c0d\u61c9\u7684\u6b77\u53f2\u8a5e\u5e8f\u5217 i H \u4e2d\u7684\u7e3d\u8a5e\u6578\uff1b \uf028 \uf029 i i H w n , \u662f i w \u5728 i H \u51fa\u73fe\u7684\u6b21 \u6578\u3002\u904e\u53bb\u8a31\u591a\u7814\u7a76\u4ea6\u5be6\u9a57\u4e86\u4e8c\u9023\u5feb\u53d6(", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "\u9ec3\u51a0\u8aa0 \u7b49", "sec_num": null }, { "text": ") | ( x y w w P \u3002\u89f8\u767c \u5c0d\u6a21\u578b\u904b\u7528\u65bc\u8a9e\u8a00\u6a21\u578b\u6642\uff0c\u662f\u7531\u5f85\u9810\u6e2c\u8a5e\u5f59 i w \u5c0d\u61c9\u7684\u6b77\u53f2\u8a5e\u5e8f\u5217 i H \u4e2d\u5c0b\u627e\u8a5e\u5f59 i w \u7684\u53ef \u80fd\u7684\u89f8\u767c\u9805 i L h h h , , , 2 1 \uf04c (\u5047\u8a2d\u6b77\u53f2\u8a5e\u5e8f\u5217 i L i h h h H , , , 2 1 \uf04c \uf03d \uff0c\u800c\u6bcf\u4e00\u500b\u6b77\u53f2\u8a5e\u5f59 l h \u5c0d \u65bc\u8a5e\u5f59 i w \u7684\u89f8\u767c\u6a5f\u7387\u70ba ) | ( l i h w P )\uff0c\u4e26\u5c07\u9019\u4e9b\u89f8\u767c\u9805\u5206\u5225\u9810\u6e2c\u7684\u689d\u4ef6\u6a5f\u7387 ) | ( l i h w P \u52d5\u614b \u7dda\u6027\u7d44\u5408\u800c\u6210\u70ba\u89f8\u767c\u5c0d\u6a21\u578b\uff1a \uf0e5 \uf02d \uf03d \uf02d \uf03d 1 1 Trigger ) | ( 1 1 ) | ( i L l l i i i i h w P L H w P (2) \u800c\u5f0f(2)\u52d5\u614b\u7522\u751f\u7684\u89f8\u767c\u5c0d\u6a21\u578b\u4ea6\u53ef\u518d\u900f\u904e\u7dda\u6027\u7d44\u5408\u65b9\u5f0f\u8207\u539f\u59cb N \u9023\u8a9e\u8a00\u6a21\u578b\u7d50\u5408\u4f86\u52d5 \u614b\u8abf\u9069\u8a9e\u97f3\u8fa8\u8b58\u6240\u9700\u7684\u8a9e\u8a00\u6a21\u578b(\u5982\u5f0f(1)\u7684\u7d50\u5408\u65b9\u5f0f)\u3002 2.3 \u4e3b\u984c\u6a21\u578b \u901a\u5e38\u5728\u8cc7\u8a0a\u6aa2\u7d22\u4efb\u52d9\u4e0a\uff0c\u4e3b\u984c\u6a21\u578b\u85c9\u7531\u4e00\u7d44\u6f5b\u85cf\u4e3b\u984c\u5206\u5e03\u7528\u4f86\u63cf\u8ff0\"\u8a5e\u5f59-\u6587\u4ef6\"\u5171\u540c\u51fa \u73fe\u7684\u7279\u6027(Blei & Lafferty, 2009)\u3002\u7576\u4e3b\u984c\u6a21\u578b\u88ab\u61c9\u7528\u81f3\u8a9e\u97f3\u8fa8\u8b58\u904e\u7a0b\u6642\uff0c\u5f85\u9810\u6e2c\u8a5e\u5f59 i w \u8207 \u5176\u5c0d\u61c9\u6b77\u53f2\u8a5e\u5e8f\u5217 i H (\u5728\u6b64\u53ef\u8996\u70ba\u4e00\u7bc7\u6587\u4ef6)\u4e4b\u76f8\u4e92\u95dc\u4fc2\u5176\u6709\u4e00\u7d44\u6f5b\u85cf\u7684\u4e3b\u984c\u5206\u5e03\u7528\u4f86 \u63cf\u8ff0\u6b77\u53f2\u8a5e\u5e8f\u5217 i H \u8207\u5f85\u9810\u6e2c\u8a5e\u5f59 i w \u5171\u540c\u51fa\u73fe\u95dc\u4fc2\uff0c\u4e0d\u518d\u662f\u55ae\u7d14\u5730\u7d93\u7531\u8a08\u7b97 i w \u5728 i H \u7684 \u51fa\u73fe\u983b\u7387\u800c\u4f30\u6e2c\uff0c\u800c\u662f\u900f\u904e i w \u51fa\u73fe\u5728\u4e0d\u540c\u6f5b\u85cf\u4e3b\u984c\u5206\u5e03\u7684\u983b\u7387\u4ee5\u53ca i H \u7522\u751f\u9019\u4e9b\u6f5b\u85cf\u4e3b \u984c\u7684\u53ef\u80fd\u6027\u4f86\u6c7a\u5b9a\uff0c\u662f\u67d0\u7a2e\u7a0b\u5ea6\u4e0a\u7684\u6982\u5ff5\u6bd4\u5c0d(Concept Matching)\u3002\u6a5f\u7387\u5f0f\u6f5b\u85cf\u8a9e\u610f\u5206\u6790 (Probabilistic Latent Semantic Analysis, PLSA)", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "\u9ec3\u51a0\u8aa0 \u7b49", "sec_num": null }, { "text": "\u6a5f\u7387\u5f0f\u6f5b\u85cf\u8a9e\u610f\u5206\u6790\u70ba\u4f8b\u4f86\u4f5c\u8aaa\u660e\uff0c\u7576\u5176\u88ab\u7528\u81f3\u8a9e\u97f3\u8fa8\u8b58\u4f86\u9032\u884c\u8a9e\u8a00\u6a21\u578b\u8abf\u9069\u6642\uff0c\u57fa\u65bc \u6b77\u53f2\u8a5e\u5e8f\u5217 i H \u4f86\u9810\u6e2c\u8a5e\u5f59 i w \u7684\u767c\u751f\u6a5f\u7387\u53ef\u8868\u793a\u70ba(Gildea & Hofmann, 1999)\uff1a ) | ( ) | ( ) | ( 1 PLSA i k K k k i i i H T P T w P H w P \uf0e5 \uf03d \uf03d (3) \u5176\u4e2d k T \u70ba\u67d0\u4e00\u500b\u6f5b\u5728\u4e3b\u984c\uff0c\u800c ) | ( k i T w P \u8207 ) | ( i k H T P \u5206\u5225\u8868\u793a\u8a5e\u5f59 i w \u767c\u751f\u5728\u4e3b\u984c k T \u7684 \u6a5f\u7387\u4ee5\u53ca\u6b77\u53f2\u8a5e\u5e8f\u5217 i H \u7522\u751f\u6b64\u4e3b\u984c\u7684\u6a5f\u7387\u3002\u6211\u5011\u5047\u8a2d\u6bcf\u4e00\u500b\u6f5b\u85cf\u4e3b\u984c\u7522\u751f\u5019\u9078\u8a5e\u7684\u6a5f \u7387 ) | ( k i T w P \u4e0d\u56e0\u8a5e\u5e8f\u5217\u641c\u5c0b\u53ca\u62d3\u5c55\u904e\u7a0b\u800c\u8b8a\u52d5\uff0c\u53ef\u5148\u85c9\u7531\u6700\u5927\u5316\u8abf\u9069(\u6216\u8a13\u7df4)\u8a9e\u6599\u767c\u751f \u6a5f\u7387\u800c\u6c42\u5f97\uff1b\u4f46\u7531\u65bc\u6b77\u53f2\u8a5e\u5e8f\u5217\u5728\u8a9e\u97f3\u8fa8\u8b58\u4e4b\u524d\u4e0d\u80fd\u4e8b\u5148\u6c7a\u5b9a\uff0c\u800c\u4e14\u6578\u91cf\u975e\u5e38\u591a\u4e26\u4e14\u6703 \u96a8\u8a9e\u97f3\u8fa8\u8b58\u904e\u7a0b\u6f14\u9032\u800c\u6539\u8b8a\uff0c\u6bcf\u4e00\u500b\u6b77\u53f2\u8a5e\u5e8f\u5217\u5c0d\u65bc\u4e3b\u984c\u5206\u5e03\u7684\u6b0a\u91cd\u5fc5\u9808\u5728\u8a9e\u97f3\u8fa8\u8b58\u904e \u7a0b\u4f7f\u7528\u671f\u671b\u503c\u6700\u5927\u5316(Expectation Maximization, EM)\u6f14\u7b97\u6cd5", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "\u9ec3\u51a0\u8aa0 \u7b49", "sec_num": null }, { "text": "\uf0e5 \uf0a2 \uf0d5 \uf0a2 \uf0e5 \uf0d5 \uf03d \uf0e5 \uf0a2 \uf0a2 \uf0e5 \uf03d \uf03d \uf0ce \uf0a2 \uf03d \uf0a2 \uf0a2 \uf0ce \uf03d \uf0ce \uf0a2 \uf0ce c c c c c L l l c L l l i c i c i i i i i i i W c", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "\u9ec3\u51a0\u8aa0 \u7b49", "sec_num": null }, { "text": ") | ( ) | ( ) | ( ) | ( ) | ( ) | ( ) | ( ) | ( ) | , ( ) | ( ) | , ( ) , | ( 1 1 WCLM (4) \u5176 W \u4ee3\u8868\u8a9e\u8005\u6240\u8b1b\u8a9e\u53e5\u6240\u6b32\u8868\u9054\u7684\u8a9e\u8a00\u8cc7\u8a0a\uff0c\u5728\u6b64\u6211\u5011\u5148\u4ee5\u8a9e\u97f3\u8fa8\u8b58\u521d\u6b65(\u7b2c\u4e00\u968e\u6bb5)\u6240 \u7522\u751f\u7684\u8a5e\u5716(Word Graph)(Ortmanns et al., 1997)\u4f86\u8fd1\u4f3c(\u8a5e\u5716\u5305\u542b\u6240\u6709\u53ef\u80fd\u7684\u5019\u9078\u8a5e\u5e8f\u5217)\uff1b \u800c c \u4ee3\u8868\u8207 W \u6240\u6b32\u8868\u9054\u7684\u8a9e\u8a00\u8cc7\u8a0a\u6709\u95dc\u7684\u4e00\u7d44\u6982\u5ff5\u95dc\u9375\u8a5e\u7d44\u3002\u5f9e\u5f0f(4)\u7684\u63a8\u5c0e\u53ef\u770b\u51fa\u8a5e\u6982 \u5ff5\u8a9e\u8a00\u6a21\u578b\u6b32\u6a21\u578b\u5316(\u7d00\u9304)\u7576\u67d0\u500b\u6982\u5ff5\u95dc\u9375\u8a5e c \u51fa\u73fe\u7684\u60c5\u6cc1\u4e0b\uff0c\u5f85\u9810\u6e2c\u8a5e\u5f59 i w \u8207\u5176\u6b77\u53f2 \u8a5e\u5e8f\u5217 i H \u5171\u540c\u51fa\u73fe\u7684\u95dc\u4fc2\u3002\u540c\u6642\uff0c\u8003\u91cf\u6a21\u578b\u4f30\u6e2c\u4e4b\u53ef\u884c\u6027\uff0c\u5f0f(4)\u9032\u4e00\u6b65\u5047\u8a2d\u7576\u67d0\u4e00\u500b \u6982\u5ff5\u95dc\u9375\u8a5e c \u51fa\u73fe\u7684\u60c5\u6cc1\u4e0b\uff0c\u5f85\u9810\u6e2c\u8a5e\u5f59 i w \u8207\u5176\u6b77\u53f2\u8a5e\u5e8f\u5217 i H \u4e2d\u4efb\u610f\u7684\u8a5e\u5f59\u4e4b\u9593\u662f\u5f7c \u6b64\u7368\u7acb\u7684\uff0c\u4e5f\u5c31\u662f\u6240\u8b02\u7684\u8a5e\u888b(Bag-of-Words)\u5047\u8a2d\u3002\u800c\u5f0f(4)\u4e2d ) | ( c w P i \u8207 ) | ( c h P l \u53ef\u5f9e \u8abf\u9069\u8a9e\u6599\u5eab\u88e1\u6982\u5ff5\u95dc\u9375\u8a5e c \u6240\u51fa\u73fe\u8655\u7684\u9130\u8fd1\u8cc7\u8a0a(Proximity Information)\uff0c\u6216\u8005\u8aaa\u662f\u51fa\u73fe \u8655\u4e0a\u4e0b\u6587\u7684\u8a5e\u5f59\u5206\u5e03\u800c\u4f30\u6e2c\u5f97\uff1b ) | ( W c P \u53ef\u900f\u904e\u9069\u7576\u65b9\u5f0f\u8a08\u7b97 W \u8207 c \u4e4b\u76f8\u4f3c\u5ea6\u800c\u6c42\u5f97\u3002 \u5be6\u52d9\u4e0a\uff0c\u6211\u5011\u9996\u5148\u906d\u9047\u5230\u7684\u554f\u984c\u5c31\u662f\u300c\u5982\u4f55\u6311\u9078\u5177\u4ee3\u8868\u6027\u7684\u95dc\u9375\u8a5e\u7d44\uff1f\u300d\u3002\u70ba\u6b64\uff0c \u672c\u8ad6\u6587\u5728\u6311\u9078\u6982\u5ff5\u95dc\u9375\u8a5e\u6642\u904b\u7528\u4e86\u5169\u968e\u6bb5\u7684\u6311\u9078\u65b9\u5f0f\uff0c\u5982\u5716 1 \u6240\u793a\u3002\u5728\u7b2c\u4e00\u968e\u6bb5\u6642\uff0c\u6211 \u5011 \u5229\u7528\u4e86\u5728\u8cc7 \u8a0a\u6aa2\u7d22\u9818\u57df \u4e4b\u4e2d\u5e38\u4f7f\u7528 \u7684\u865b\u64ec\u95dc\u806f \u56de\u994b (Pseudo-Relevance Feedback, PRF)(Baeza-Yates & Ribeiro-Neto, 2011) \uff0c \u4e26 \u5229 \u7528 \u57fa \u65bc \u5eab \u723e \u8c9d \u514b \u2500 \u840a \u4f2f \u52d2 \u5dee \u7570 \u91cf (Kullback-Leibler Divergence, KL-Divergence) \u4e4b \u67e5 \u8a62 \u8207 \u6587 \u4ef6 \u6a21 \u578b \u5316 \u6280 \u8853", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "\u9ec3\u51a0\u8aa0 \u7b49", "sec_num": null }, { "text": "EQUATION", "cite_spans": [], "ref_spans": [], "eq_spans": [ { "start": 0, "end": 8, "text": "EQUATION", "ref_id": "EQREF", "raw_str": "\uf0ee \uf0ed \uf0ec \uf03e \uf0b4 \uf02b \uf03d ohterwise f if n N f w m j j m j m j 0 0 ) / log( ) log 1 ( , , ,", "eq_num": "(5)" } ], "section": "\u9ec3\u51a0\u8aa0 \u7b49", "sec_num": null }, { "text": "EQUATION", "cite_spans": [], "ref_spans": [], "eq_spans": [ { "start": 0, "end": 8, "text": "EQUATION", "ref_id": "EQREF", "raw_str": "\u4f7f\u7528\u6982\u5ff5\u8cc7\u8a0a\u65bc\u4e2d\u6587\u5927\u8a5e\u5f59\u9023\u7e8c\u8a9e\u97f3\u8fa8\u8b58\u4e4b\u7814\u7a76 53 \u5716 1. \u8a5e\u6982\u5ff5\u8a9e\u8a00\u8a9e\u8a00\u6a21\u578b\u6d41\u7a0b\u5716 \u4e0a\u8ff0\u7684\u8a5e\u983b\u8207\u53cd\u5411\u6587\u4ef6\u983b\u7387\u5206\u6578\u4e3b\u8981\u53ef\u5206\u70ba\u5169\u500b\u4e3b\u8981\u90e8\u5206\uff1a\u7b2c\u4e00\u90e8\u5206\u70ba ) log 1 ( ,m j f \uf02b \uff0c \u5176\u4e2d\u7684 m j f , \u5247\u4ee3\u8868\u8a5e\u5f59 j w \u5728\u6b64\u6587\u4ef6 m d \u4e2d\u6240\u51fa\u73fe\u7684\u6b21\u6578\uff0c\u7a31\u4e4b\u70ba\u8a5e\u983b(Term Frequency, TF)\uff0c\u53ef\u4ee5\u89e3\u91cb\u70ba\u5177\u8d8a\u9ad8\u8a5e\u983b\u7684\u8a5e\u5f59\u5c0d\u6587\u4ef6\u4f86\u8b1b\u8d8a\u91cd\u8981\uff1b\u7b2c\u4e8c\u90e8\u5206\u70ba ) / log( j n N \uff0c\u5176\u4e2d j n \u4e4b\u5247\u662f\u4ee3\u8868\u8a5e\u5f59 j w \u51fa\u73fe\u5728\u6240\u6709\u865b\u64ec\u95dc\u806f\u6587\u4ef6\u7684\u6587\u4ef6\u500b\u6578\uff0c\u7a31\u4e4b\u70ba\u53cd\u5411\u6587\u4ef6\u983b\u7387(Inverse Document Frequency, IDF)\uff0c\u7576\u67d0\u4e00\u8a5e\u5f59\u51fa\u73fe\u50c5\u51fa\u73fe\u5728\u5c11\u6578\u7684\u6587\u4ef6\u4e4b\u4e2d\uff0c\u5247\u6b64\u8a5e\u5f59\u8d8a\u5177\u6709 \u7368\u7279\u6027\u3002\u6211\u5011\u671f\u671b\u900f\u904e\u5f0f(5)\u80fd\u627e\u51fa\u5177\u6709\u91cd\u8981\u6027\u8207\u7368\u7279\u6027\u7684\u8a5e\u5f59\u505a\u70ba\u6982\u5ff5\u95dc\u9375\u8a5e\u3002 3.2 \u4ee5\u7fa4\u805a\u9762\u5411\u5efa\u7acb\u6982\u5ff5\u8a9e\u8a00\u6a21\u578b \u7fa4\u805a\u6982\u5ff5\u8a9e\u8a00\u6a21\u578b(Cluster-based Concept Language Model, CCLM)\u5047\u8a2d\u5728\u8abf\u9069\u8a9e\u6599\u7684\u6587 \u4ef6\u96c6\u5167\u4e4b\u6587\u4ef6\u53ef\u4ee5\u7531\u4e00\u7d44\u6982\u5ff5\u985e\u5225 C \u4f86\u8868\u793a\uff0c\u85c9\u7531\u8a9e\u8005\u8b1b\u7684\u8a9e\u53e5\u6240\u6b32\u8868\u9054\u7684\u8a9e\u8a00\u8cc7\u8a0a W \u8207\u9019\u4e9b\u6982\u5ff5\u985e\u5225\u7684\u500b\u5225\u95dc\u806f\u7a0b\u5ea6\u4f86\u7372\u5f97\u8a9e\u53e5\u53ef\u80fd\u7684\u6982\u5ff5\u5206\u5e03\uff0c\u4e26\u505a\u70ba\u8a9e\u8a00\u6a21\u578b\u9810\u6e2c\u7684\u6839 \u64da\uff1a \uf0e5 \uf0d5 \uf0a2 \uf0a2 \uf0e5 \uf0d5 \uf03d \uf0e5 \uf0a2 \uf0a2 \uf0e5 \uf03d \uf0ce \uf0a2 \uf03d \uf0a2 \uf0a2 \uf0ce \uf03d \uf0ce \uf0a2 \uf0ce C C C C C L l l C L l l i C i C i i i i i i W C P C h P W C P C h P C w P W C P C H P W C P C H w P W H w P 1 1 1 - CCLM ) | ( ) | ( ) | ( ) | ( ) | ( ) | ( ) | ( ) | ( ) | , ( ) , | ( (6) \u5176 \u4e2d \u6982 \u5ff5 \u985e \u5225 \u7684 \u6c42 \u53d6 \u53ef \u900f \u904e \u4e00 \u822c \u5206 \u7fa4 \u6f14 \u7b97 \u6cd5 \u8af8 \u5982 K-Means \u6f14 \u7b97 \u6cd5 (Baeza-Yates & Ribeiro-Neto, 2011)\u800c\u6c42\u5f97\uff1b ) | ( W C P \u53ef\u57fa\u65bc\u5c07\u8a9e\u8a00\u8cc7\u8a0a W \u8207\u6bcf\u4e00\u500b\u6982\u5ff5\u985e\u5225 C \u8868\u793a\u6210\u5411 \u91cf\u5f62\u5f0f\uff0c\u8a08\u7b97 W \u8207 C \u4e4b(\u9918\u5f26)\u76f8\u4f3c\u5ea6\u800c\u6c42\u5f97\uff1b ) | ( C w P i \u4ee3\u8868\u6982\u5ff5\u985e\u5225 C \u9810\u6e2c\u8a5e\u5f59 i w \u7684\u55ae \u9023\u8a9e\u8a00\u6a21\u578b\u6a5f\u7387\uff0c\u53ef\u900f\u904e\u6700\u5927\u5316\u76f8\u4f3c\u6a5f\u7387\u4f30\u6e2c\u800c\u5f97(Zhai, 2008)\u3002\u5f9e\u5f0f(6)\u7684\u63a8\u5c0e\u53ef\u770b\u51fa\u7fa4 \u805a\u6982\u5ff5\u8a9e\u8a00\u6a21\u578b\u6b32\u6a21\u578b\u5316(\u7d00\u9304)\u7576\u67d0\u4e00\u500b\u6982\u5ff5\u985e\u5225 C \u51fa\u73fe\u7684\u60c5\u6cc1\u4e0b\uff0c\u5f85\u9810\u6e2c\u8a5e\u5f59 i w \u8207\u5176 \u6b77\u53f2\u8a5e\u5e8f\u5217 i H \u5171\u540c\u51fa\u73fe\u7684\u95dc\u4fc2\u3002 54 \u90dd\u67cf\u7ff0 \u7b49 \u5716 2. \u7fa4\u805a\u6982\u5ff5\u8a9e\u8a00\u6a21\u578b\u793a\u610f\u5716 \u6211\u5011\u53ef\u4ee5\u5c07\u5f0f(6)\u4e2d\u6982\u5ff5\u985e\u5225 C \u9810\u6e2c\u8a5e\u5f59 i w \u7684\u8a9e\u8a00\u6a21\u578b\u5ef6\u4f38\u6210\u70ba\u96d9\u9023(Bigram)\u6216\u8005 \u4e09\u9023(Trigram)\u8a9e\u8a00\u6a21\u578b\uff0c\u800c\u53ef\u5206\u5225\u5f97\u5230\u4e0b\u9762\u5169\u500b\u8868\u793a\u5f0f\uff1a \uf0e5 \uf0d5 \uf0a2 \uf0a2 \uf0a2 \uf0e5 \uf0d5 \uf03d \uf0ce \uf0a2 \uf03d \uf0a2 \uf02d \uf0a2 \uf0a2 \uf0ce \uf03d \uf02d C C C L l l l C L l l l L i i i i i W C P C h h P C h P W C P C h h P C h P C h w P W H w P 2 1 1 2 1 1 2 - CCLM ) | ( ) , | ( ) | ( ) | ( ) , | ( ) | ( ) , | ( ) , | ( (7) \uf0e5 \uf0d5 \uf0a2 \uf0a2 \uf0a2 \uf0a2 \uf0e5 \uf0d5 \uf03d \uf0ce \uf0a2 \uf03d \uf0a2 \uf02d \uf02d \uf0ce \uf03d \uf02d \uf02d \uf02d C C C L l l l l C L l l l l L L i i i i i W C P C h h h P C h h P C h P W C P C h h h P C h h P C h P C h h w P W H w P 3 1 2 1 2 1 3 1 2 1 2 1 1 3 - CCLM ) | ( ) , , | ( ) , | ( ) | ( ) | ( ) , , | ( ) , | ( ) | ( ) , , | ( ) , | (", "eq_num": "(8" } ], "section": "\u9ec3\u51a0\u8aa0 \u7b49", "sec_num": null }, { "text": "Computer assistant language learning (CALL) offers many advantages which differ from a traditional classroom setting where one teacher is responsible for a group of students. CALL allows learners to decide and adjust the level and pace of learning individually by. Another advantage that the classroom setting could not provide is unlimited access of on-line high-quality comparison between speech produced by a learner and a native speaker. By far the most popular CALL systems are computer-assisted pronunciation teaching (CAPT) system based on automatic speech recognition (ASR) outcome. The goals of CAPT are automatic diagnosis of pronunciation including specific or global error (Witt & Young, 2000; Coniam, 1999; Moustroufas & Digalakis, 2007) , but the focus has been on segmental errors. However, in recent years studies focusing on suprasegmentals have shown that in addition to segmental information, prosodic information is in fact indispensable. Specifically, when detailed information of the consonant and vowel segments in the speech signal is removed, results show how listeners pay attention to prosodic features such as the pitch variation, rhythm alternation, loudness change as well as intonation. The resulting speech without any segmental and lexical content suggests that listeners are also sensitive to prosodic information (Scruton, 1996; Trofimovich & Baker, 2006; Munro, 1995) . This has led to more research attention to investigate prosody in relation to comprehensibility and accent of native vs. non-native speech; and a more balanced understanding regarding the contribution from both the segmental and suprasegmental aspects of language (Derwing & Munro, 1997; Anderson-Hsieh et al., 1992; Munro & Derwing, 1999 , Celce-Murcia et al., 1996 Derwing et al., 1998) . Reported studies that applied prosodic training for second-language (L2) learners have demonstrated that computer-assisted prosody training systems did improve the overall comprehensibility of L2 speech (Hardison, 2004; Hirata, 2004) . These studies showed prosody training with a real-time pitch display could improve both prosody and segmental accuracy, as judged by native speaker raters, while similar effect is found for English-speaking learners of Japanese. Another study demonstrated that aligning Mandarin English duration patterns with native English using resynthesis technology and dynamic time warping also brought significant increase in intelligibility (Tajima et al., 1997) . Complementary findings are studies that showed how incorrect timing and stress patterns are often cited as major contributors to intelligibility deficit (Benrabah, 1997; Anderson-Hsieh et al., 1992) . However, it appears that considerable gap does exist between research findings and software development. CALL systems are usually criticized as not necessarily \"linguistically and pedagogically sound\" (Derwing & Munro, 2005; Neri et al., 2002) . For example, a study specifically states that most CALL programs were developed with little understanding of phonology and how to apply phonological knowledge to teaching (Pennington, 1999) . In short, there is less understanding of L2 prosody, and even less CALL systems that have applied features of L2 prosody into the English, which is part of AESOP that was designed and constructed to represent to include various kinds of L2 English spoken in Asia (Visceglia et al., 2009) with built-in linguistic knowledge (Anderson-Hsieh et al., 1992) . Built-in linguistic knowledge in the corpus design is to elicit characteristics which are predicted to be present in L2 English speech. Our previous studies have catalogued a series of TW L2 features that may impede intelligibility. The series of studies to TW L2 accent started from prosodic under-differentiation which is not only found in syntax-elicited narrow focus but also in lexicon-defined word stress. Acoustic analysis of syntax-elicited narrow focus also showed that TW L2's production of narrow focus is less robust in F0 and amplitude than L1 (Visceglia et al., 2011; Visceglia et al., 2012) . Further investigations of lexical-stress prosody showed the degree of contrast in F0 and amplitude is again less robust, making word stress in TW L2 English less differentiable (Tseng et al., 2012) . The above two studies showed that lack of pitch and loudness contrasts is one of major feature of TW L2 accent in both word and sentence prosody. Further analysis revealed more complex L1s' features in words that may be difficult for TW L2 speakers (Tseng & Su, 2014) . Native (L1) speakers may choose to realize word stress through binary stress/no-stress contrast anchored by the position of primary stress. Post-primary syllables are reduced to near-tertiary stress while pre-primary syllables are elevated to near-primary magnitude in F0. The 3-way primary/secondary/tertiary contrast is merged into a binary stress/no-stress contrast with robust prosodic contrast between the primary stress and its following syllable(s). As expected, the position-related merge of the secondary word stress is difficult for TW L2 speakers.", "cite_spans": [ { "start": 685, "end": 705, "text": "(Witt & Young, 2000;", "ref_id": "BIBREF58" }, { "start": 706, "end": 719, "text": "Coniam, 1999;", "ref_id": "BIBREF32" }, { "start": 720, "end": 750, "text": "Moustroufas & Digalakis, 2007)", "ref_id": "BIBREF43" }, { "start": 1348, "end": 1363, "text": "(Scruton, 1996;", "ref_id": "BIBREF49" }, { "start": 1364, "end": 1390, "text": "Trofimovich & Baker, 2006;", "ref_id": "BIBREF51" }, { "start": 1391, "end": 1403, "text": "Munro, 1995)", "ref_id": "BIBREF44" }, { "start": 1670, "end": 1693, "text": "(Derwing & Munro, 1997;", "ref_id": "BIBREF35" }, { "start": 1694, "end": 1722, "text": "Anderson-Hsieh et al., 1992;", "ref_id": "BIBREF29" }, { "start": 1723, "end": 1744, "text": "Munro & Derwing, 1999", "ref_id": "BIBREF45" }, { "start": 1745, "end": 1772, "text": ", Celce-Murcia et al., 1996", "ref_id": "BIBREF31" }, { "start": 1773, "end": 1794, "text": "Derwing et al., 1998)", "ref_id": "BIBREF37" }, { "start": 2000, "end": 2016, "text": "(Hardison, 2004;", "ref_id": "BIBREF39" }, { "start": 2017, "end": 2030, "text": "Hirata, 2004)", "ref_id": "BIBREF40" }, { "start": 2465, "end": 2486, "text": "(Tajima et al., 1997)", "ref_id": "BIBREF50" }, { "start": 2642, "end": 2658, "text": "(Benrabah, 1997;", "ref_id": "BIBREF30" }, { "start": 2659, "end": 2687, "text": "Anderson-Hsieh et al., 1992)", "ref_id": "BIBREF29" }, { "start": 2891, "end": 2914, "text": "(Derwing & Munro, 2005;", "ref_id": "BIBREF36" }, { "start": 2915, "end": 2933, "text": "Neri et al., 2002)", "ref_id": "BIBREF46" }, { "start": 3107, "end": 3125, "text": "(Pennington, 1999)", "ref_id": "BIBREF48" }, { "start": 3391, "end": 3415, "text": "(Visceglia et al., 2009)", "ref_id": "BIBREF55" }, { "start": 3451, "end": 3480, "text": "(Anderson-Hsieh et al., 1992)", "ref_id": "BIBREF29" }, { "start": 4040, "end": 4064, "text": "(Visceglia et al., 2011;", "ref_id": "BIBREF56" }, { "start": 4065, "end": 4088, "text": "Visceglia et al., 2012)", "ref_id": "BIBREF57" }, { "start": 4268, "end": 4288, "text": "(Tseng et al., 2012)", "ref_id": "BIBREF57" }, { "start": 4540, "end": 4558, "text": "(Tseng & Su, 2014)", "ref_id": "BIBREF52" } ], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "In addition to the above prosodic difference found between L1and TW L2 English, we also compared TW L2 accent and TW Mandarin, the target L2 speakers' mother tongue, and found in what ways TW L2 accent could be attributed to their L1 Mandarin features (Nguyen et al., 2008) . Following this line of research, TW Mandarin is also included in the present study to further examine if and how some TW L2 English accent can further be attributed to Mandarin.", "cite_spans": [ { "start": 252, "end": 273, "text": "(Nguyen et al., 2008)", "ref_id": "BIBREF47" } ], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "The present study aims to incorporate prosodic features found to contribute to TW L2 accent, and try to conduct prosody classification among L1 English, L2 English and Ll Mandarin by machine learning technology. The aim is to test if L1 English, L2 English and Ll Mandarin could be discriminated from each other by integrated prosodic features elicited by syntax-induced narrow focus and lexicon-defined word stress. Further discrimination analysis compares distinct prosodic characteristics of TW L2_Eng and TW L2_Eng-L1_Man shared characteristics of prosody to verify if prosodic features of TW L2_Eng are in relation to Mandarin. In addition, speaker-pair similarity by prosodic patterns is computed to test (1) difference between L1 English and TW L2 English groups and (2) cohesion within L1 English/TW L2 English group.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "Read speech of Native English (L1_Eng), Taiwan L2 English (L2_Eng), Taiwan Mandarin (L1_Man) are used in present analysis. The materials of English speech are 5 reading tasks from the AESOP-ILAS recoded by 9 L1 (4M&5F) and 9 L2 (5M&4F) speakers. These 5 tasks are designed to elicit production of English segmental and suprasegmental characteristics including: (1) word-level features such as segmental by target words in carrier sentence; (2) phrase boundary phenomena such as declarative falls and interrogative rises by target words at phrase boundaries 3form, timing and location of pitch accents, which are used to create phrasal and sentential prominence (broad and narrow focus) by target words in narrow focus position. 20 target words with 2-, 3-and 4-syllable of all possible stress patterns (Appendix A) are embedded in Task1 to Task 3. (4) function words in stressed and unstressed positions and (5) prosodic disambiguation of syntactic structures.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Speech Data", "sec_num": "2." }, { "text": "In section 3.1 and 3.2, the sentences in task 1 to task 5 are used for prosody classification among L1_Eng, L2_Eng and Ll_Man. In section 3.3, lexicon-defined prosodic similarity among speakers is computed by 20 stress-balanced target words in carrier sentence, Task1, to eliminate effect from higher level. An example of target word marked in boldface in carrier sentence is as follow.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Speech Data", "sec_num": "2." }, { "text": "I said SUPERMARKET five times.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "\u2022", "sec_num": null }, { "text": "The sentences with broad and narrow focus in task 3 are used to test syntax-elicited prosodic similarity among speakers. An example of sentence in which broad and narrow focus are embedded is as follow. Narrow focus and broad focus are marked in boldface and italic respectively.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "\u2022", "sec_num": null }, { "text": "\u2022 No. I usually buy fruit at the SUPERMARKET because they stay open later.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Context: Do you buy fruit at the farmer's market?", "sec_num": null }, { "text": "After selecting sentences with acceptable F0 extraction, 369 L1_Eng and 434 L2_Eng sentences are used in present analysis.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Context: Do you buy fruit at the farmer's market?", "sec_num": null }, { "text": "The material of L1_Man is intonation balanced speech corpus (3441MB, 31:10) in SINICA COSPRO (Tseng et al., 2003) which aims to examine role of intonation with respect to prosodic grouping in Mandarin speech. 3 types of sentences including declarative, interrogative and exclamatory with balanced POS combination are designed and collected in this corpus. In order to compare with English materials (task1 and task3 in AESOP-ILAS) in which all sentences are declarative, only declarative sentences are included in present analysis. Speech of one male and one female with good recording quality are chosen for analysis. After further selecting sentences with acceptable F0 tracking, 288 L1_Man declarative sentences are used in present analysis. Prosodic words in Mandarin are adopted as units of word-layer segmentation and corresponding feature extraction.", "cite_spans": [ { "start": 93, "end": 113, "text": "(Tseng et al., 2003)", "ref_id": "BIBREF54" } ], "ref_spans": [], "eq_spans": [], "section": "Context: Do you buy fruit at the farmer's market?", "sec_num": null }, { "text": "All data were pre-processed automatically for segmental alignment using the HTK Toolkit, which was then manually spot-checked by trained transcribers for accuracy. F0 values were extracted and measured using a semitone scale.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Annotation", "sec_num": "2.1" }, { "text": "Prosodic features used in present study are F0, duration, intensity. Each feature is z-normalized by sentence first then each sentence is encoded as a feature vector representing prosodic characteristics with hierarchical structure by sentence and word layer. The higher-level features, namely sentence-level features are derived by average of features in subsidiary units, namely word while word-level features are computed by subsidiary phoneme. In addition to conventional 6 types of general feature representation including mean, standard deviation, maximum, minimum, range and pairwise contrast referring to PVI (Grabe & Low, 2002) by each feature and each layer, histogram representation is also adopted to show more detailed properties of feature distribution. The adoption of histogram representation also could overcome inconsistent dimension among sentences which derived from varied number of words and phonemes thus requirement of consistent dimension could be fulfilled for classifier input. Two prosodic features encoded by histogram representation are mean and pairwise contrast by subsidiary units in sentence and word layer. Present histogram representation encodes prosodic features with 7 bins in which distribution of units is normalized to 100%. Normalized duration and F0 values were further refined to remove intrinsic physical properties based on previous knowledge. The intrinsic physical property for duration denotes segmental duration of each phoneme and intrinsic physical property for F0 denotes intonation of each sentence. 200 prosodic features in total are used in the present study.", "cite_spans": [ { "start": 617, "end": 636, "text": "(Grabe & Low, 2002)", "ref_id": "BIBREF38" } ], "ref_spans": [], "eq_spans": [], "section": "Feature Extraction", "sec_num": "3.1" }, { "text": "Two popular classifiers for prosody classification among L1_Eng, L2_Eng and Ll_Man used are introduced as follows.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Classification", "sec_num": "3.2" }, { "text": "The principle of k-nearest-neighbor classifier coded as KNNC (Cortes & Vapnik, 1995) is based on concept that data instances of the same class should be nearer in the feature space. As a result, for a given unknown data point x, the class is determined by K nearest points of x. The principles compute the distance between x and all the data points in the training space to decide K which is used for assign/predict class of unknown data point x.", "cite_spans": [ { "start": 61, "end": 84, "text": "(Cortes & Vapnik, 1995)", "ref_id": "BIBREF34" } ], "ref_spans": [], "eq_spans": [], "section": "KNNC", "sec_num": "3.2.1" }, { "text": "Given a set of data with each example in data marked by binary categories, a support vector machine (SVM) (Coomans & Massart, 1982) training algorithm builds a model that assigns examples into one category or the other as accurate as possible while examples of the separate categories are divided by a clear gap that is as wide as possible. Unknown data points are then predicted to belong to a category based on which side of the gap they fall on.", "cite_spans": [ { "start": 106, "end": 131, "text": "(Coomans & Massart, 1982)", "ref_id": "BIBREF33" } ], "ref_spans": [], "eq_spans": [], "section": "SVM", "sec_num": "3.2.2" }, { "text": "Discrimination analysis is conducted between pair of speaker group by 200 prosodic features described in section 3.1. P value (Lehmann, 1997) is adopted as discriminative indicator between pair of speaker group. In a statistical test, sample results are compared to likely population conditions by way of two competing hypotheses: the \"null hypothesis\" is a neutral statement about \"no difference\" between two groups; the other, the \"alternative hypothesis\" is the statement that the person performing the test would like to conclude if the data will allow it. The p-value is the probability of obtaining the observed sample results when the null hypothesis is actually true. It could be quantified by the conditional probability Pr(X|H) (X is a random variable representing the observed data and H is the statistical hypothesis under consideration) which gives the likelihood of the observation if the hypothesis is assumed to be correct. If this p-value is very small, it suggests that the observed data is different from the assumption that the null hypothesis is true, and thus that hypothesis must be rejected and the other hypothesis accepted as true.", "cite_spans": [ { "start": 126, "end": 141, "text": "(Lehmann, 1997)", "ref_id": "BIBREF42" } ], "ref_spans": [], "eq_spans": [], "section": "Discrimination Analysis by Prosodic Features", "sec_num": "3.3" }, { "text": "The similarity is defined by cosine measure between any two of L1/L2 speakers by prosodic patterns of word/sentence. The value of point (i, j) in the matrix denotes cosine distance between speaker i and speaker j. In following section, the matrix is represented by a plot with Some Prosodic Characteristics of Taiwan English Accent 67 i\u00d7j grids in which shading value of each grid denotes value of point (i, j). The darker the color is, the more similar between speakers i and j.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Similarity Comparison by Prosodic Patterns", "sec_num": "3.4" }, { "text": "In order to test if L1 English, TW L2 English and TW L1 Mandarin could be identified from each other by prosody, classification is conducted and performance is computed by 2 classifiers, SVM/KNNC. Average recognition rate is 91.57% by SVM and 81.86% by KNNC respectively. Figure 1 shows recognition rate in form of confusion matrix by best classifier, SVM and results suggest L1_Eng with most distinct characteristic with the others, L2_Eng and L1_Man. L1_Eng could be 100% identified from L2_Eng and L1_Man; however, only 88.97% of L2_Eng and 84.74% of L1_Man could be recognised from the others. Further binary classification is conducted between L2_Eng and L1_Man and shows best recognition rate 86.03% by SVM. Figure 2 shows confusion matrix which demonstrates only 88.05% of L2_Eng and 82.99% of L1_Man could be identified from each other.", "cite_spans": [], "ref_spans": [ { "start": 272, "end": 280, "text": "Figure 1", "ref_id": "FIGREF4" }, { "start": 714, "end": 722, "text": "Figure 2", "ref_id": "FIGREF5" } ], "eq_spans": [], "section": "Prosody Classification among L1_Eng, L2_Eng and Ll_Man", "sec_num": "4.1" }, { "text": "The above results suggest that L1_Eng could be differentiated from L2_Eng and L1_Man; however, confusion is found between L2_Eng and L1_Man. In other words, L1_Eng is distinct from L2_Eng and L1_Man prosodically; on the other hand, L2_Eng and L1_Man share some common prosodic characteristics which differentiate from L1_Eng. In the following section, discrimination analysis is conducted by prosodic features to show distinct prosodic characteristics of L2_Eng from L1_Eng and common prosodic characteristics between L2_Eng and L1_Man. Table 1 shows most distinct prosodic characteristics between L2_Eng and L1_Eng. After pairwise discrimination analysis between L2_Eng and L1_Man is conducted by each prosodic feature, the most discriminative features are computed and listed in Table1. Results show most discriminative prosodic features by lowest 5 p-values in L2_Eng vs. L2_Eng are 'mean by normalized F0', 'minimum by normalized F0', 'mean by normalized volume', 'maximum by normalized volume' and 'stand deviation by normalized duration' in sentence layer and maximum/PC/stand deviation/range/histogram_dimension#3 by normalized volume in word layer. Table 2 shows common prosodic characteristics between L2_Eng and L1_Man. Pairwise discrimination between L2_Eng and L1_Man is conducted by prosodic feature and most similar features are listed in Table 2 . Results show most similar prosodic features by highest 5 p-values by L2_Eng vs. L1_Man are 'histogram_dimension#5 by pairwise contrast of normalized volume', 'histogram_dimension#1&3 by pairwise contrast of normalized duration', ' histogram_dimension#5 by normalized duration without intrinsic properties' and 'pairwise contrast by normalized F0' in sentence layer and 'mean by normalized F0', 'range by normalized volume', 'histogram_dimension#2 by f0 without intonation effect', 'histogram_dimension#6 by normalized F0'and 'histogram_dimension#7 by normalized volume in word layer.", "cite_spans": [], "ref_spans": [ { "start": 537, "end": 544, "text": "Table 1", "ref_id": "TABREF0" }, { "start": 1157, "end": 1164, "text": "Table 2", "ref_id": "TABREF2" }, { "start": 1353, "end": 1360, "text": "Table 2", "ref_id": "TABREF2" } ], "eq_spans": [], "section": "Discussion", "sec_num": "4.1.1" }, { "text": "The results show F0/duration/volume in sentence layer and volume in word layer contribute to TW L2 accent. By discrimination analysis between L2_Eng and L1_Man, results demonstrate F0/duration/volume in sentence layer and F0/volume in word layer are shared L2_Eng-L1_Man prosodic properties. We further assume that distinct features of L2 accent might attribute to prosodic characteristics borrowed from their mother tongue, namely L1_Man thus distinct features of L2Eng are compared with L2Eng-L1Man shared features. The results show distinct L2_Eng features do overlap with L2Eng-L1Man common features. Comparison by sentence layer shows similar features found coexisting in L2Eng-L1Eng distinct features and L2Eng-L1Man common features (green in Table 1 and Table 2 ) are stand deviation by normalized duration in L1Eng-L2Eng distinct features and histogram_dimension#1&3 by pairwise contrast of normalized duration in L2Eng-L1Man common features. Pairwise contrast is defined by between-phone variation and the property is similar to stand deviation representing global variation; thus we could regard them as overlap. In summary, the results suggest tempo contrast by syntax-elicited narrow focus in sentence layer and loudness range by lexicon-defined word stress in word layer are distinct L2 features of TW English which might attribute to prosodic transfer of Mandarin, namely L2s' mother tongue.", "cite_spans": [], "ref_spans": [ { "start": 749, "end": 768, "text": "Table 1 and Table 2", "ref_id": "TABREF0" } ], "eq_spans": [], "section": "Discussion", "sec_num": "4.2.1" }, { "text": "In addition to analysis by individual prosodic feature in section 3.2, similarity is computed between any two of L1/L2 speakers by prosodic patterns of word/sentence. After between-speaker similarity is derived, we examine if between-speaker similarity is greater when they are in the same speaker group. The aim is to test if consistency within each speaker group (L1/L2) and discrimination between L1 and L2 could be found. Figure 3 , 4 and 5 show similarity matrix between any two of L1/L2 speakers by prosodic patterns of word. First row by normalized duration in Figure 3 demonstrates by color lightness, first L1 speaker is more similar with speaker 1 to speaker 9 than speaker 10 to speaker 18 which represent L1 speakers and L2 speakers respectively. In addition, the left-top block by green dotted cross demonstrates L1 speakers with more consistency within group than the other blocks. It suggests L1 with greater cohesion/consistency than right-top (L1 vs. L2), left-bottom (L1 vs. L2) and right-bottom (L2 vs. L2). Right-bottom (L2 vs. L2) block also shows secondary consistent which is darker than right-top (L1 vs. L2), left-bottom (L1 vs. L2). It suggests L2s' prosodic patterns are consistent as well. Normalized duration without intrinsic properties in Figure3 further shows that removing intrinsic duration could further help to discriminate L1 and L2.", "cite_spans": [], "ref_spans": [ { "start": 426, "end": 434, "text": "Figure 3", "ref_id": "FIGREF6" }, { "start": 568, "end": 576, "text": "Figure 3", "ref_id": "FIGREF6" } ], "eq_spans": [], "section": "Similarity Comparison by Prosodic Patterns", "sec_num": "4.3" }, { "text": "Figure 4 also shows great cohesion within speaker group (L1&L2) respectively and great difference between speaker group (L1 vs. L2) by normalized F0 and normalized F0 without intonation effect; however, removing intonation appears not to improve L1-L2 discrimination significantly.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Figure 3. The similarity between any two of L1/L2 speakers by duration patterns in word layer. Color bars show the more dark the color, the more similar between two speakers. The value of point (i,j) in the matrix represents cosine distance between i and j that diagonal indicates self-similarity with darkest color. The green dotted cross represents boundary between L1 and L2 speakers.", "sec_num": null }, { "text": "layer. Figure 5 shows similarity matrix by normalized intensity. Results show no significant discrimination found between L1 and L2.", "cite_spans": [], "ref_spans": [ { "start": 7, "end": 15, "text": "Figure 5", "ref_id": "FIGREF16" } ], "eq_spans": [], "section": "Figure 4. The similarity between any two of L1/L2 speakers by F0 patterns in word", "sec_num": null }, { "text": "word layer.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Figure 5. The similarity between any two of L1/L2 speakers by intensity patterns in", "sec_num": null }, { "text": "By between-speaker similarity of word by duration/F0, the two distinct blocks by shading value representing L1s' and L2s' patterns are found. It suggests between-speaker similarity by word layer is greater when they are in the same speaker group. In other words, L1 and L2 produce respective timing/pitch patterns of word with great within-group consistency but within-group features are distinct from counterpart group. Between-group discrimination and within-group consistency is not found by loudness patterns. The results suggests timing/pitch patterns elicited by lexicon-defined word stress in word layer are distinct L2 features of TW English. Figure 6 , 7 and 8 show similarity matrix between any two of L1/L2 speakers by prosodic patterns of sentence. By Figure 6 and 7, no significant discrimination between L1 and L2 is found by normalized duration, normalized duration without intrinsic properties, normalized F0 and normalized F0 without intonation. ", "cite_spans": [], "ref_spans": [ { "start": 651, "end": 659, "text": "Figure 6", "ref_id": "FIGREF9" }, { "start": 764, "end": 772, "text": "Figure 6", "ref_id": "FIGREF9" } ], "eq_spans": [], "section": "Discussion", "sec_num": "4.3.1.1" }, { "text": "By intensity similarity of sentence, the two distinct blocks by shading value representing L1s' and L2s' patterns are found. It suggests between-speaker similarity by intensity of sentence is greater when they are in the same speaker group. In other words, L1 and L2 produce respective prosodic patterns with great within-group consistency but within-group features are discriminative to counterpart group. Between-group discrimination and within-group consistency is not found by timing/pitch patterns. The results suggest loudness patterns elicited by syntax-induced narrow focus in sentence layer are distinct L2 feature of TW English.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Discussion", "sec_num": "4.3.2.1" }, { "text": "The present study examines prosodic characteristics of Taiwan English in relation to native English and Mandarin, mother tongue of TW speakers. Prosody classification among native English, TW L2 English and TW Mandarin is conducted by machine learning technology and results show Taiwan L2 English is found to be distinct from L1 English in prosody. However, TW L2 English and Taiwan Mandarin share some common prosodic characteristics which differentiate them from L1_Eng. Further comparison by each prosodic feature shows distinct L2 features of TW English can be attributed to prosodic transfer of Mandarin is tempo contrast elicited by syntax-induced narrow focus in sentence layer and loudness range by lexicon-defined stress in word layer. By examining prosodic patterns of word/sentence, similarity analysis suggests that between-speaker similarity is greater when they are in the same speaker group in both word and sentence layer. In other words, L1 and L2 speakers produce respective prosodic patterns with great within-group consistency but their within-group patterns are discriminative to counterpart group by loudness patterns in sentence layer and timing/pitch patterns in word layer. We believe the above study with incorporated linguistic knowledge not only sheds light on better understanding of TW L2 English, but can also be applied CALL system implementation. Future works will include providing prosody evaluation matrix of L2 by word and by sentence with degree measures of similarity and improvement scoring so that L2 learners will become more sensitive to prosody features.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Discussion and Conclusion", "sec_num": "5." }, { "text": "Previous studies show that preterm infants are prone to immaturity of neurological development which leads to their sensitiveness toward pain stimulation, and the greater pain they suffer would reflect on cry production. If a set of distinctive measures can be identified, it might be possible to differentiate infant cries due to organic pathology and cries in the spectrum of normative behavior, including infant colic which is frequently found in infants \uf02a Department of Foreign Languages and Literature, National Cheng Kung University, TAIWAN Email: leemay@mail.ncku.edu.tw younger than 4 months of age. The measures can thus be used to support doctors' diagnosis to identify if the unknown cries are caused by just infant colic or other more complicated factors in order to provide appropriate care. Cry utterances were analyzed with long-time average spectrum (LTAS) in two groups of newborn infants in this study. Non-partitioned cry episode and the 3 equal-length partitions (P1, P2, P3) were analyzed. First spectral peak, mean spectral energy, spectral tilt, and high frequency energy, as well as unedited cry duration and percent phonation were measured.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "Colic strikes infants who are under four months old, and it makes the infants cry in the evening on a daily bases or at the moment of waking up (Lester etal., 1990) . The cause of this pain is still unknown (Zeskind & Barr, 1997) . Colic occurs when infants are around one month old and it often disappears without a reason when infants are older than three months (Clifford, 2002) . It is a universal and commonly-seen phenomenon which is the cause for excessive cry behavior. Though previous studies suggested that higher fundamental frequency and a larger percentage of dysphonation in cry could be found in the pain cries of infants who suffered from colic, no standard acoustic features in cry utterance of infants with colic was established (Zeskind & Barr, 1997) . Long-time average spectrum might provide an option to investigate if there are any significant characteristics in the cries of infants with colic.", "cite_spans": [ { "start": 144, "end": 164, "text": "(Lester etal., 1990)", "ref_id": null }, { "start": 207, "end": 229, "text": "(Zeskind & Barr, 1997)", "ref_id": "BIBREF78" }, { "start": 365, "end": 381, "text": "(Clifford, 2002)", "ref_id": "BIBREF60" }, { "start": 747, "end": 769, "text": "(Zeskind & Barr, 1997)", "ref_id": "BIBREF78" } ], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "Though infants are not able to talk, they can express their feelings and emotions through cry, facial expression, and body movement. Diseases are able to be discovered by some characteristics in cry production (Radhika et al., 2012) . For example, different pain stimuli would lead to different fundamental frequencies in infant cry utterance (Radhika et al., 2012) . If more specific characteristics are found in certain diseases, it would be more effective in prescribing and curing. Sometimes parents can differentiate why their babies cry by their various cry production (Soltis, 2004) . As for the way of eliciting cries, Johnston, Stevens, Craig, and Grunau (1993) proposed two different ways: the heel-stick procedure and injection. In this current study, injection was used as the only standard method to elicit cry to avoid any nuances that might caused by the different types of pain stimuli. However, even though there are measures to quantify the pain intensity infants endure, the experience of pain is quite subjective and is not merely related to physiological but also psychological factors (Qiu, 2006) . Moreover, since infants use cry to arouse caregivers' attention, it can be expected that infants' cry utterance differs with and without their caregivers around them (Greenet et al., 1995) . Usually, the responses from caregivers bring cry behavior to a halt (Green et al., 1995) . Cry is thus a way of drawing others' attention to help infants get rid of the uncomfortable situation or meet their needs (LaGasse et al., 2005) . Therefore, cry is not only an independent behavior but also plays an important role in social interactions between infants and their caretakers (Green et al., 1995) .", "cite_spans": [ { "start": 210, "end": 232, "text": "(Radhika et al., 2012)", "ref_id": "BIBREF74" }, { "start": 343, "end": 365, "text": "(Radhika et al., 2012)", "ref_id": "BIBREF74" }, { "start": 575, "end": 589, "text": "(Soltis, 2004)", "ref_id": "BIBREF75" }, { "start": 627, "end": 670, "text": "Johnston, Stevens, Craig, and Grunau (1993)", "ref_id": "BIBREF65" }, { "start": 1107, "end": 1118, "text": "(Qiu, 2006)", "ref_id": "BIBREF73" }, { "start": 1287, "end": 1309, "text": "(Greenet et al., 1995)", "ref_id": null }, { "start": 1380, "end": 1400, "text": "(Green et al., 1995)", "ref_id": "BIBREF64" }, { "start": 1525, "end": 1547, "text": "(LaGasse et al., 2005)", "ref_id": "BIBREF66" }, { "start": 1694, "end": 1714, "text": "(Green et al., 1995)", "ref_id": "BIBREF64" } ], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "Because of the immature development of nervous systems caused by premature birth, cry production of preterm infants is believed to reveal different characteristics from that of term infants whose nervous system is comparatively well-developed. Premature infants were reported to have higher fo in their cry utterance, and it might be due to the immature, and shorter vocal folds (Johnston et al., 1993) . Or as Zeskind (1983) stated that high-risk infants were not able to perfectly control their cry production and that they tended to react more intensely towards pain stimuli than did low-risk infants. Infants react differently to the same stimulus pain whether they are healthy or born at risk. However, while some studies reported that preterm infants were more sensitive to pain stimuli, others found that some premature infants had less intense reactions towards pain than normal infants (Qiu, 2006) .", "cite_spans": [ { "start": 379, "end": 402, "text": "(Johnston et al., 1993)", "ref_id": "BIBREF65" }, { "start": 411, "end": 425, "text": "Zeskind (1983)", "ref_id": "BIBREF79" }, { "start": 895, "end": 906, "text": "(Qiu, 2006)", "ref_id": "BIBREF73" } ], "ref_spans": [], "eq_spans": [], "section": "Quantitative Assessment of Cry in Term and Preterm Infants: 79 Long-Time Average Spectrum Analysis", "sec_num": null }, { "text": "The main objective of this current study is to find out how the cry production between term and preterm infants differs from each other. The findings might help in detecting infants' health conditions. Moreover, if the difference of the cry utterance can be systematically characterized, the measurements can be further applied to identify features in neonate cry due to infant colic.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Quantitative Assessment of Cry in Term and Preterm Infants: 79 Long-Time Average Spectrum Analysis", "sec_num": null }, { "text": "Previous studies indicated that gender did not lead to significant differences in first spectral peak, mean spectral energy, spectral tilt, and high frequency energy (Goberman & Robb, 1999; Goberman et al., 2008) . Therefore, gender was not controlled in this study. There were 26 infant participants; 16 were term infants and the other 10 were preterm infants. The infants were all under four months old for both term infants and preterm infants according to their gestational ages. All of the infants in this study were considered to have normal hearing according to interview with parents.", "cite_spans": [ { "start": 166, "end": 189, "text": "(Goberman & Robb, 1999;", "ref_id": "BIBREF62" }, { "start": 190, "end": 212, "text": "Goberman et al., 2008)", "ref_id": "BIBREF63" } ], "ref_spans": [], "eq_spans": [], "section": "Participants", "sec_num": "2.1" }, { "text": "For collecting cry utterance of both preterm and term infants, TASCAM wave recorder and RODE uni-directional microphone were used in audio recording. The microphone was held near the infants' mouth. All infants were in the supine position while receiving the injection. This can also avoid influence of different postures in acoustic properties, for example, fundamental frequency (Lin & Green, 2007) . The cry production of both groups of infants was recorded during and after they received the injection in the hospital. The pain stimulus was thus the same in both groups of infants.", "cite_spans": [ { "start": 381, "end": 400, "text": "(Lin & Green, 2007)", "ref_id": "BIBREF68" } ], "ref_spans": [], "eq_spans": [], "section": "Data Collection", "sec_num": "2.2" }, { "text": "The analysis in this current study was mainly based on Goberman and Robb (1999) . A cry episode of infants was defined as the duration of the continuous cry utterance, beginning with the first audible cry utterance after the pain stimulus, and an episode was completed as soon as the infants stopped cry. The non-voiced parts of a cry episode were first edited out in the cry utterance, making a \"non-partitioned cry episode\" (Goberman & Robb, 1999) . In this current study, the inspiratory cry was eliminated, and only the phonatory parts were analyzed. Then, a non-partitioned episode was divided into three partitions with the same length of durations (P1, P2, P3). P1, P2, P3 are regarded as the early, middle, and late sections of the cry episode, respectively, corresponding to the attack, cruise, and subdual phases of a cry episode as suggested by Truby and Lind (1965) . Unedited cry duration, percent phonation, first spectral peak, mean spectral energy, spectral tilt, and high frequency energy were measured. \uf0b7 First spectral peak (FSP): the first amplitude peak across the LTAS display.", "cite_spans": [ { "start": 55, "end": 79, "text": "Goberman and Robb (1999)", "ref_id": "BIBREF62" }, { "start": 426, "end": 449, "text": "(Goberman & Robb, 1999)", "ref_id": "BIBREF62" }, { "start": 856, "end": 877, "text": "Truby and Lind (1965)", "ref_id": "BIBREF77" } ], "ref_spans": [], "eq_spans": [], "section": "Acoustic Analysis", "sec_num": "2.3" }, { "text": "\uf0b7 Mean spectral energy (MSE): the mean amplitude value from 0 to 8000 Hz. Average energy from 0 to 8000 Hz -first peak energy \uf0b7 Spectral tilt (ST): the ratio of energy between 0-1000 Hz, and 1000-5000 Hz. Average energy from 1000 to 5000 Hz / average energy from 0 to1000 Hz \uf0b7 High frequency energy (HFE): the sum of amplitudes from 5000 to 8000 Hz. Average energy from 5000 to 8000 Hz *(8000-5000) / the bandwidth of LTAS ", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Acoustic Analysis", "sec_num": "2.3" }, { "text": "Cry duration reveals respiratory capability, and term infants were thus expected to have longer cry duration than preterm infants (Cacace et al., 1995; Michelsson et al., 1982; Thoden et al., 1985) . In this current study, the average duration of cry episodes for the 16 term infants was 42.27s (SD = 31.27s), and for the 10 preterm infants was 36.21s (SD = 30.93s). As expected, term infants had longer average duration of cry episodes. However, a t test was performed to examine whether cry duration differed statistically between these two groups, and indicated no significant difference between term and preterm infants, t(24) = 0.48, two-tailed, p = 0.63. The result is the same as that of Goberman and Robb (1999) .", "cite_spans": [ { "start": 130, "end": 151, "text": "(Cacace et al., 1995;", "ref_id": "BIBREF59" }, { "start": 152, "end": 176, "text": "Michelsson et al., 1982;", "ref_id": "BIBREF72" }, { "start": 177, "end": 197, "text": "Thoden et al., 1985)", "ref_id": "BIBREF76" }, { "start": 695, "end": 719, "text": "Goberman and Robb (1999)", "ref_id": "BIBREF62" } ], "ref_spans": [], "eq_spans": [], "section": "Unedited Cry Duration", "sec_num": "3.1" }, { "text": "The amount of cries in term infants was reported to be larger than that in preterm infants (Cacace et al., 1995; Michelsson et al., 1982; Thoden et al., 1985) . The percentage of cry utterance in a long-term non-partitioned, unedited cry episode was calculated in this current study. However, no significant difference in percent phonation was found between these two groups in this current study. The average percent phonation across the cry episodes of the 16 term infants and the 10 preterm infants was 67.25% (SD = 17.04) and 67% (SD = 13.98) respectively. That is, 67% of the unedited cry episode contained cry production. Like what was found in Goberman and Robb (1999) , there was no significant difference across groups in the percentage of cry utterance, t(24) = 0.039, two-tailed, p = 0.97.", "cite_spans": [ { "start": 91, "end": 112, "text": "(Cacace et al., 1995;", "ref_id": "BIBREF59" }, { "start": 113, "end": 137, "text": "Michelsson et al., 1982;", "ref_id": "BIBREF72" }, { "start": 138, "end": 158, "text": "Thoden et al., 1985)", "ref_id": "BIBREF76" }, { "start": 651, "end": 675, "text": "Goberman and Robb (1999)", "ref_id": "BIBREF62" } ], "ref_spans": [], "eq_spans": [], "section": "Percent Phonation", "sec_num": "3.2" }, { "text": "The non-partitioned and partitioned first spectral peak values of the 16 term and the 10 preterm infants are listed in Table 1 and illustrated in Figure 2 . A two-way analysis of variance (ANOVA) was performed to calculate if there were significant differences in FSP values between the two groups (term factor), and whether there was significant variation between the three equal-length cry durations (P1, P2, P3) in each group (partition factor). The results indicated no significant term by partition interaction (p = 0.64), no significant main effect for term status (p = 0.17), and no significant main effect for partition (p = 0.56). Despite the fact that there was no significant difference in statistical tests, from overall observation, term infants demonstrated higher FSP in non-partitioned and the three partitioned episodes than that in preterm infants. Moreover, term and preterm infants displayed different trends of FSP in P1, P2, and P3. Term infants' cry episode involved more distinct phases with decrease of FSP in P3, whereas FSP kept increasing from P1 to P3 in preterm infants.", "cite_spans": [], "ref_spans": [ { "start": 119, "end": 126, "text": "Table 1", "ref_id": "TABREF0" }, { "start": 146, "end": 154, "text": "Figure 2", "ref_id": "FIGREF5" } ], "eq_spans": [], "section": "First Spectral Peak (FSP)", "sec_num": "3.3" }, { "text": "While the infants were receiving injections, the sharp pain stimulated them and all the infants burst out to cry. According to the previous studies (Johnston et al., 1993; Goberman & Robb, 1999) , preterm infants were expected to have higher FSP because preterm infants were thought to be more sensitive and would react more intensely to pain. Intensive cry causes the increase of the subglottal pressure and the stiffness of the vocal folds. Premature infants, compared to term infants, were thus reported to have higher fo in their cry phonation due to tension of the larynx. However, this difference was not found in this current study. The mean FSP of the term infants turned out to be higher than that of the preterm infants, in both the non-partitioned episode and the three equal-length episodes. Nevertheless, the difference between these two groups was not statistically significant as mentioned above. More data with controlled methodology in future studies can verify the discrepancy of the findings.", "cite_spans": [ { "start": 148, "end": 171, "text": "(Johnston et al., 1993;", "ref_id": "BIBREF65" }, { "start": 172, "end": 194, "text": "Goberman & Robb, 1999)", "ref_id": "BIBREF62" } ], "ref_spans": [], "eq_spans": [], "section": "First Spectral Peak (FSP)", "sec_num": "3.3" }, { "text": "Another distinction between these two groups was the changes of FSP across three partitions. The trend of increase followed by decrease of FSP in term infants was not found in preterm infants. FSP kept increasing in preterm infants over time. This distinction was also found in Goberman and Robb (1999) , in which FSP decreased significantly in term infants and Quantitative Assessment of Cry in Term and Preterm Infants: 83 Long-Time Average Spectrum Analysis there was no reduction of FSP in preterm infants.", "cite_spans": [ { "start": 278, "end": 302, "text": "Goberman and Robb (1999)", "ref_id": "BIBREF62" }, { "start": 396, "end": 424, "text": "Term and Preterm Infants: 83", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "First Spectral Peak (FSP)", "sec_num": "3.3" }, { "text": "The mean spectral energy values of non-partitioned and partitioned episodes of the 16 term and the 10 preterm infants are shown in Table 2 and Figure 3 . P1, P2, P3) ", "cite_spans": [], "ref_spans": [ { "start": 131, "end": 138, "text": "Table 2", "ref_id": "TABREF2" }, { "start": 143, "end": 151, "text": "Figure 3", "ref_id": "FIGREF6" }, { "start": 154, "end": 165, "text": "P1, P2, P3)", "ref_id": "FIGREF4" } ], "eq_spans": [], "section": "Mean Spectral Energy (MSE)", "sec_num": "3.4" }, { "text": "A two-way analysis of variance (ANOVA) was performed to investigate if there were significant differences between term and preterm infants (term factor), as well as across P1, P2, and P3 (partition factor) in each group. The results indicated no significant term by partition interaction (p = 0.36). There was a significant main effect for partition (F = 6.47, p = 0.003), yet there was no significant main effect for term, p = 0.52. One-way ANOVA tests were then performed in each group to check the changes of MSE in P1, P2, and P3. In term infants, P2 was significantly higher than P3 (p = 0.029). In preterm infants, P1 showed significantly higher energy than P2 (p =0.042) and P3 (p = 0.012). MSE refers to the average energy in the frequency range of 0-8000 Hz, which was indicated to correspond to tension of the laryngeal musculature (Fuller & Horii, 1988) . In this current study, although no significant difference could be identified, preterm infants showed higher MSE in non-partitioned episode and the three equidurational cry episodes. This shows that during the cry duration, the preterm infants' laryngeal muscles were tighter and they had a more severe reaction toward pain stimulus. The tighter laryngeal muscles suggested a more intense cry production. This finding was also indicated in Goberman and Robb (1999) . Moreover, a decrease of MSE over time could be observed in both term and preterm infants. This might suggest that the laryngeal muscles of both groups of infants loosened by phase, especially in preterm infants. There was a sharper decrease of MSE from P1 to P3 in preterm infants. The trend seemed to correspond to the distinct phases in a cry episode indicated in Truby and Lind (1965) with the attack phase (high amplitude) and the cruising phase followed by the subdual phase (the lowest period of stress).", "cite_spans": [ { "start": 842, "end": 864, "text": "(Fuller & Horii, 1988)", "ref_id": "BIBREF61" }, { "start": 1307, "end": 1331, "text": "Goberman and Robb (1999)", "ref_id": "BIBREF62" }, { "start": 1700, "end": 1721, "text": "Truby and Lind (1965)", "ref_id": "BIBREF77" } ], "ref_spans": [], "eq_spans": [], "section": "Figure 3. Mean spectral energy in term and preterm infants over time (P1, P2, and P3 are three equal-length partitioned cry episodes.)", "sec_num": null }, { "text": "The spectral tilt values of non-partitioned and partitioned cry episodes of the two groups are listed in Table 3 and displayed in Figure 4 . In order to evaluate if there were significant differences of ST between the two groups and whether there were significant variations between the three equal-length cry durations (P1, P2, P3) in each group, a two-way analysis of variance (ANOVA) was performed. There was no significant term by partition interaction (p = 0.223), no significant main effect for partition (p = 0.994), and no significant main effect for term (p = 0.123). To investigate changes in ST across partitions within each group, separate one-way ANOVA tests were performed for term and preterm infant groups. In term infants, post hoc comparisons identified a significantly higher ST in P2 than in P3 (p = 0.003), but no significant difference in ST across partitions in the preterm infants.", "cite_spans": [], "ref_spans": [ { "start": 105, "end": 112, "text": "Table 3", "ref_id": "TABREF3" }, { "start": 130, "end": 138, "text": "Figure 4", "ref_id": "FIGREF15" } ], "eq_spans": [], "section": "Spectral Tilt (ST)", "sec_num": "3.5" }, { "text": "Spectral tilt measures the ratio of low frequency energy and high frequency energy, revealing how quickly the energy declines over time. The quicker the decline is, the larger the ratio. Overall, the term infants showed higher ST values at the onset of cry production which decreased across partitions, whereas the preterm infants had lower ST values at the onset, which increased over time. That is, there was a quicker reduction of energy across partitions in preterm infants. The ST of term infants did not increase over time as mentioned in Goberman and Robb (1999) , on the contrary, the increase of ST was found in preterm infants. A higher ST value was reported to be related to hypoadduction of the vocal folds, and a lower ST reflects a hyperadduction of the vocal folds (Mendoza et al., 1996) . In this current study, hyperadduction was observed in the decrease of ST in term infants, whereas hypoadduction was observed in the increase of ST in preterm infants.", "cite_spans": [ { "start": 545, "end": 569, "text": "Goberman and Robb (1999)", "ref_id": "BIBREF62" }, { "start": 780, "end": 802, "text": "(Mendoza et al., 1996)", "ref_id": "BIBREF70" } ], "ref_spans": [], "eq_spans": [], "section": "Table 3. Spectral tilt from the non-partitioned episodes (NP) and three partitioned cry episodes with equal length (P1, P2, P3) in the term and preterm infants", "sec_num": null }, { "text": "The high frequency energy values of non-partitioned and partitioned cry episodes of the two groups are listed in Table 4 and illustrated in Figure 5 . In order to identify if there was significant variation of HFE between term and preterm infants, and whether there were significant variations between the three equal-length cry durations (P1, P2, P3) in each group, a two-way analysis of variance (ANOVA) was performed. No significant term by partition interaction (p = 0.805) was found. Like in Goberman and Robb (1999) , there was no main effect for term (p = 0.962). That is, there was no significant difference in HFE across the two groups. There was significant main effect for partitions (F = 8.29, p = 0.001). One-way ANOVA tests were then performed to check changes in HFE across partitions within each group. Significant differences in HFE were found across partitions for both term infants (F = 3.91, p = 0.031) and for preterm infants (F = 4.57, p = 0.025). There was a significantly higher P1 in HFE than P3 in both infant groups (p = 0.029 in term infants, and p = 0.02 in preterm infants). In both groups, HFE decreased over time. The HFE of term infants did not change drastically over time; however, in preterm infants, the HFE showed a steep descent, crossing from 1807 to 1227. HFE measures the energy in the range of 5000-8000 Hz, which was indicated to be related to the noise elements in phonation (e.g., irregular cry utterance). It was reported that dysphonation in infant cry was very likely related to neurological disorders (Mende, Herzel, & Wermke, 1990) . However, no significant difference of HFE between groups was found in this current study. Further studies with more data from both term and preterm infants might verify the correspondence of HFE and its physiological bases.", "cite_spans": [ { "start": 497, "end": 521, "text": "Goberman and Robb (1999)", "ref_id": "BIBREF62" }, { "start": 1551, "end": 1582, "text": "(Mende, Herzel, & Wermke, 1990)", "ref_id": "BIBREF69" } ], "ref_spans": [ { "start": 113, "end": 120, "text": "Table 4", "ref_id": "TABREF17" }, { "start": 140, "end": 148, "text": "Figure 5", "ref_id": "FIGREF16" } ], "eq_spans": [], "section": "High Frequency Energy (HFE)", "sec_num": "3.6" }, { "text": "Cry productions of 16 term infants and 10 preterm infants under 4 months of age were analyzed with long-time average spectrum (LTAS). Major findings were:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Summary and Suggestion for Future Studies", "sec_num": "4." }, { "text": "1. There was no significant difference between term and preterm infants in cry duration.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Summary and Suggestion for Future Studies", "sec_num": "4." }, { "text": "However, term infants had longer overall cry duration, which corresponded to better 2. There was no significant difference across groups in the percentage of cry utterance although previous studies indicated that the amount of cries in term infants was larger than that in preterm infants;", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Summary and Suggestion for Future Studies", "sec_num": "4." }, { "text": "3. No significant variation was found between these two groups in FSP. Term infants showed overall higher FSP, which is different from previous findings. Moreover, FSP in term infants involved more distinct phases across three partitions, declining toward the end of cry episode;", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Summary and Suggestion for Future Studies", "sec_num": "4." }, { "text": "4. There was no significant difference of MSE between term and preterm infants. Overall, preterm infants showed higher MSE, which corresponded to tighter laryngeal muscle and intense cry production. A decrease of MSE was found in both groups over time;", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Summary and Suggestion for Future Studies", "sec_num": "4." }, { "text": "5. No significant variation was found between these two groups in ST. There was a quicker reduction of energy with larger ST in preterm infants over time, which revealed hypoadduction of the vocal folds;", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Summary and Suggestion for Future Studies", "sec_num": "4." }, { "text": "6. There was no significant difference in HFE between two groups, and there was a significant decline of HFE over time in both term and preterm infants.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Summary and Suggestion for Future Studies", "sec_num": "4." }, { "text": "Some of the results in this current study did not match the findings in previous studies. The differences could be due to a few discerning variables. First, although the uni-directional microphone was used in this study, the environmental noises could not be completely controlled because the nurses were required to explain the procedure to the caregivers. Moreover, there was unavoidable overlapping from noises of cry from other infants. Once infant cry overlapped with adults' voice or cry from other infants, the partitions could no longer be used for further analysis. Second, all the infants receiving injections had their caregivers around. Both term and preterm infants might use more strength in cry, hoping their caretakers would alleviate their pain. This caused inevitable interaction between adults and infants, bringing unexpected disturbance to the results. Third, some caretakers tended to soothe the infants as soon as they started to cry, which would significantly change the natural cry episode since the soothing and consolation from the caretakers might influence their cry production. The infants might feel safe and stopped crying. This might cause incomplete early, middle, and late sections in a cry episode, as Goberman and Robb (1999) mentioned. In further studies, the environmental noise (e.g., from nurses, parents, and other infants around) should be controlled. Moreover, video recording should be implemented in order to identify whether the infants stopped crying spontaneously or their attention was drawn by things around. By controlling disturbance, future study can acquire sufficient data to identify systematic distinction in the pattern of cry production between term and preterm infants. Furthermore, LTAS analysis utilized in this study for cry analysis can be automatically ", "cite_spans": [ { "start": 1238, "end": 1262, "text": "Goberman and Robb (1999)", "ref_id": "BIBREF62" } ], "ref_spans": [], "eq_spans": [], "section": "Summary and Suggestion for Future Studies", "sec_num": "4." }, { "text": "http://www.abgeordnetenwatch.de", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "Note that Sinica Corpus had ceased to update around 17 years ago.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "http://ecfa.speaking.tw/imho.php 5 http://140.112.147.131/PTT/6 https://github.com/amigcamel/Jseg", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "Yi-An Wu and Shu-Kai Hsieh", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "\u8a73\u898b www.nlm.nih.gov/bsd/policy/structured_abstracts.html", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "http://www.nltk.org 3 http://www.nactem.ac.uk/GENIA/tagger/", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null } ], "back_matter": [ { "text": "This research was supported by a grant from National Science Council (NSC102-2410-H-006 060) and the Ministry of Education, Taiwan, R.O.C. The Aim for the Top University Project to the National Cheng Kung University. We thank the infant participants and their families for their time and cooperation.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Acknowledgements", "sec_num": null }, { "text": "This index covers all technical items---papers, correspondence, reviews, etc.---that appeared in this periodical during 2014.The Author Index contains the primary entry for each item, listed under the first author's name. The primary entry includes the coauthors' names, the title of paper or other item, and its location, specified by the publication volume, number, and inclusive pages. The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication volume, number, and inclusive pages. ", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Vol. 19", "sec_num": null }, { "text": "Please send application to:The ", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "To Register\uff1a", "sec_num": null } ], "bib_entries": { "BIBREF0": { "ref_id": "b0", "title": "Estimating party policy positions: Comparing expert surveys and hand-coded content analysis", "authors": [ { "first": "K", "middle": [], "last": "Benoit", "suffix": "" }, { "first": "M", "middle": [], "last": "Laver", "suffix": "" } ], "year": 2007, "venue": "Electoral Studies", "volume": "26", "issue": "1", "pages": "90--107", "other_ids": {}, "num": null, "urls": [], "raw_text": "Benoit, K., & Laver, M. (2007). Estimating party policy positions: Comparing expert surveys and hand-coded content analysis. 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Facilitating and promoting academic research, seminars, training, discussions, comparative evaluations and other activities related to computational linguistics.", "links": null }, "BIBREF95": { "ref_id": "b95", "title": "Collecting information and materials on recent developments in the field of computational linguistics, domestically and internationally", "authors": [], "year": null, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Collecting information and materials on recent developments in the field of computational linguistics, domestically and internationally.", "links": null }, "BIBREF96": { "ref_id": "b96", "title": "Publishing pertinent journals, proceedings and newsletters", "authors": [], "year": null, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Publishing pertinent journals, proceedings and newsletters.", "links": null }, "BIBREF97": { "ref_id": "b97", "title": "Setting of the Chinese-language technical terminology and symbols related to computational linguistics", "authors": [], "year": null, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Setting of the Chinese-language technical terminology and symbols related to computational linguistics.", "links": null }, "BIBREF98": { "ref_id": "b98", "title": "Maintaining contact with international computational linguistics academic organizations", "authors": [], "year": null, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Maintaining contact with international computational linguistics academic organizations.", "links": null }, "BIBREF99": { "ref_id": "b99", "title": "Dealing with various other matters related to the development of computational linguistics", "authors": [], "year": null, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Dealing with various other matters related to the development of computational linguistics.", "links": null } }, "ref_entries": { "FIGREF0": { "uris": null, "text": ".-H., Huang, C.-Y., & Su, Y.-S. (2012). Chinese Postal Address and Associated Information Extraction. The 26th Annual Conference of the Japanese Society for Artificial Intelligence, 2012.", "type_str": "figure", "num": null }, "FIGREF1": { "uris": null, "text": "Wu and Shu-Kai Hsieh in the next section.", "type_str": "figure", "num": null }, "FIGREF2": { "uris": null, "text": "Opinion Toward CSSTA: A Text Mining Approach 23 Establish the model for the classifier.", "type_str": "figure", "num": null }, "FIGREF4": { "uris": null, "text": "Word clouds for supporting and opposing keywrods.", "type_str": "figure", "num": null }, "FIGREF5": { "uris": null, "text": "The trend of the topic popularity.(For the interactive figure, please click here.)", "type_str": "figure", "num": null }, "FIGREF6": { "uris": null, "text": "The trend of the supporting rate.(For the interactive figure, please click here.)", "type_str": "figure", "num": null }, "FIGREF7": { "uris": null, "text": "\u7814 \u7a76 \u8a08 \u756b (MOST 103-2221-E-003-016-MY2, NSC 103-2911-I-003-301, NSC 101-2221-E-003-024-MY3 \u3001 NSC 101-2511-S-003-057-MY3 \u3001 NSC 101-2511-S-003-047-MY3 \u548c NSC 102-2221-E-003-014-MY3)\u4e4b\u7d93\u8cbb\u652f\u6301\uff0c\u8b39\u6b64\u81f4\u8b1d\u3002 Liu, S.-H., Chu, F.-H.,Lin, S.-H., Lee, H.-S., & Chen, B. (2007). Training data selection for improving discriminative training of acoustic models. In Proceedings of IEEE workshop on Automatic Speech Recognition and Understanding, 284-289. Mikolov, T., Karafi\u00e1t, M., Burget, L., \u010cernock\u00fd, J., & Khudanpur, S. (2010). Recurrent neural network based language model. In Proceedings of the Annual Conference of the International Speech Communication Association, 1045-1048. Ortmanns, S., Ney, H., & Aubert, X. (1997). A word graph algorithm for large vocabulary continuous speech recognition. Computer Speech and Language, 11, 43-72. O'Shaughnessy, D., Deng, L., & Li, H. (2013). Speech information processing: Theory and applications. Proceedings of the IEEE, 101(5), 1034-1037. Potapenko, A., & V. Konstantin. (2013). Robust PLSA performs better than LDA. In Proceedings of the European Conference on Information Retrieval, 784-787. Rosenfeld, R. (2000). Two decades of statistical language modeling: Where do we go from here? Proceedings of IEEE, 88(8), 2000, 1270-1278. www.speech.sri.com/projects/srilm/.Tam, Y., & Schultz, T. (2005). Dynamic language model adaptation using variational Bayes inference. In Proceedings of the Annual Conference of the International Speech Communication Association, 5-8. Troncoso, C., & Kawahara, T. (2005). Trigger-based language model adaptation for automatic meeting transcription. In Proceedings of the Annual Conference of the International Speech Communication Association, 1297-1300. Wang, H.-M., Chen, B., Kuo, J.-W., & Cheng, S.-S. (2005). MATBN: a Mandarin Chinese broadcast news corpus. International Journal of Computational Linguistics & Chinese Language Processing, 10(1), 219-235. Zhai, C. X. (2008). Statistical language models for information retrieval: A critical review. Foundations and Trends in Information Retrieval, 2(3), 137-213.", "type_str": "figure", "num": null }, "FIGREF8": { "uris": null, "text": "is developed from the above discussed background and aims to analyze prosodic characteristics of TW L2 English accent supported by linguistic knowledge. The speech data used in the present study is AESOP-ILAS (Asian English Speech cOrpus Project collected by the Institute of Linguistics, Academia Sinica) representing accent of Taiwan L2", "type_str": "figure", "num": null }, "FIGREF9": { "uris": null, "text": "The similarity between any two of L1/L2 speakers by duration patterns in sentence layer.", "type_str": "figure", "num": null }, "FIGREF10": { "uris": null, "text": "The similarity between any two of L1/L2 speakers by F0 patterns in sentence layer.", "type_str": "figure", "num": null }, "FIGREF11": { "uris": null, "text": "shows intensity patterns of sentence with great within-group cohesion and great between-group difference in both L1 and L2.", "type_str": "figure", "num": null }, "FIGREF12": { "uris": null, "text": "Similarity between any two of L1/L2 speakers by intensity patterns in sentence layer.", "type_str": "figure", "num": null }, "FIGREF13": { "uris": null, "text": "Typical LTAS display showing the location of the first spectral peak (FSP) and high frequency energy (HFE) between 5000Hz and 8000Hz.Quantitative Assessment of Cry inTerm and Preterm Infants: 81 Long-Time Average Spectrum Analysis", "type_str": "figure", "num": null }, "FIGREF14": { "uris": null, "text": "First spectral peak in term and preterm infants over time (P1, P2, and P3 are three equal-length partitioned cry episodes.)", "type_str": "figure", "num": null }, "FIGREF15": { "uris": null, "text": "Spectral tilt in term and preterm infants over time (P1, P2, and P3 are three equal-length partitioned cry episodes.) Quantitative Assessment of Cry in Term and Preterm Infants: 85 Long-Time Average Spectrum Analysis", "type_str": "figure", "num": null }, "FIGREF16": { "uris": null, "text": "High frequency energy in term and preterm infants over time (P1, P2, and P3 are three equal-length partitioned cry episodes.)", "type_str": "figure", "num": null }, "TABREF0": { "text": "\u7dd2\u8ad6 \u6839\u64da\u570b\u969b\u6578\u64da\u8cc7\u8a0a IDC \u65bc 2013 \u5e74 9 \u6708\u8abf\u67e5\u5831\u544a\u986f\u793a\uff0c\u5e73\u677f\u96fb\u8166\u7684\u51fa\u8ca8\u91cf\u5728 2013 \u5e74\u7b2c\u56db \u5b63\u9996\u6b21\u8d85\u904e\u500b\u4eba\u96fb\u8166\uff0c\u800c\u667a\u6167\u578b\u624b\u6a5f\u4e0d\u8ad6\u5728\u51fa\u8ca8\u91cf\u6216\u5e02\u4f54\u7387\u65e9\u5c31\u9060\u9060\u8d85\u904e\u684c\u4e0a\u578b\u96fb\u8166\u548c \u53ef\u651c\u5f0f\u96fb\u8166\u7684\u7e3d\u548c\uff0cIDC \u751a\u81f3\u9810\u6e2c\u5e73\u677f\u96fb\u8166\u7684\u51fa\u8ca8\u91cf\u5c07\u5728 2015 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\u6703(29%)\u4ee5\u53ca\u8cfc\u5c4b\u3001\u79df\u5c4b\u8cc7\u8a0a(28%)\u3002\u7136\u800c\u7576\u4f7f\u7528\u8005\u5728\u96fb\u5b50\u5730\u5716\u4e0a\u641c\u5c0b\u9019\u4e9b\u5730\u9ede\u540d \u7a31 (POI\uff0cPoint of Interest)\u6642\uff0c\u7d93\u5e38\u7121\u6cd5\u627e\u5230\uff0c\u56e0\u70ba\u96fb\u5b50\u5730\u5716\u4e0a\u96d6\u6709\u5730\u9ede\u540d\u7a31\u6a19\u8a3b\uff0c\u4f46\u662f \u76f8\u95dc\u8cc7\u8a0a\u4e0d\u8db3\uff0c\u800c\u9019\u4e9b\u8cc7\u8a0a\u5176\u5be6\u5927\u591a\u53ef\u4ee5\u5728\u7db2\u9801\u4e2d\u627e\u5230\u3002\u56e0\u6b64\u4f7f\u7528\u8005\u5927\u591a\u5fc5\u9808\u958b\u555f\u700f\u89bd \u5668\u641c\u5c0b\u5546\u5bb6\u540d\u7a31\u627e\u51fa\u5730\u5740\uff0c\u4e26\u628a\u5730\u5740\u8f38\u5165\u81f3\u96fb\u5b50\u5730\u5716\u67e5\u8a62\u8def\u7dda\u3002\u4f46\u884c\u52d5\u88dd\u7f6e\u87a2\u5e55\u5c0f\uff0c\u4e14 \u8f38\u5165\u6587\u5b57\u4e0d\u4fbf\u5229\uff0c\u5982\u679c\u8981\u53cd\u8986\u67e5\u8a62\u5c07\u662f\u4e00\u4ef6\u8017\u6642\u8017\u529b\u7684\u5de5\u4f5c\u3002\u5982\u679c\u9019\u6642\u5019\u6709\u4e00\u500b\u5546\u5bb6\u5730 \u7406\u8cc7\u8a0a\u7cfb\u7d71\u80fd\u4e8b\u5148\u5c07\u7db2\u8def\u4e0a\u7684\u5546\u5bb6\u8cc7\u8a0a\u9032\u884c\u6574\u5408\uff0c\u6700\u5f8c\u63d0\u4f9b\u4e00\u500b APP \u76f4\u63a5\u8b93\u4f7f\u7528\u8005\u67e5\u8a62\uff0c \u5c07\u53ef\u4ee5\u5927\u5e45\u5ea6\u6e1b\u5c11\u4f7f\u7528\u8005\u8207\u88dd\u7f6e\u9593\u7684\u4e92\u52d5\u6b21\u6578\uff0c\u6709\u6548\u7684\u63d0\u4f9b\u641c\u5c0b\u7684\u4fbf\u5229\u6027\u3002 \u70ba\u5efa\u69cb\u5546\u5bb6\u5730\u7406\u8cc7\u6599\u5eab\uff0cChuang \u7b49\u4eba(Chuang et al., 2014)\u5c0d\u65bc\u5305\u542b\u5730\u5740\u7db2\u9801\u63d0\u51fa\u4ee5 \u5ee3\u5ea6\u512a\u5148\u641c\u5c0b\u3001\u9ec3\u9801\u722c\u87f2\u3001\u8207\u5730\u5740\u6a23\u7248\u67e5\u8a62\u4e09\u7a2e\u6293\u53d6\u7a0b\u5f0f\uff0c\u4e26\u5229\u7528 Chang \u7b49\u4eba(Chang et al., 2012)\u7684\u5730\u5740\u64f7\u53d6\u7a0b\u5f0f\uff0c\u53d6\u5f97\u5927\u91cf\u4e2d\u6587\u5730\u5740\u3002Li \u8207 Chang(2009)\u4e26\u5b9a\u7fa9\u5730\u5740\u76f8\u95dc\u8cc7\u8a0a\u64f7\u53d6 \u554f\u984c\uff0c\u5e0c\u671b\u85c9\u6b64\u8c50\u5bcc\u6bcf\u500b POI \u7684\u76f8\u95dc\u8cc7\u8a0a\uff0c\u63d0\u9ad8\u5730\u7406\u8cc7\u8a0a\u6aa2\u7d22(Geographical Information Retrieval, GIR)\u7684\u53ec\u56de\u7387\u3002\u7136\u800c\u4e0d\u8ad6\u662f Li \u8207 Chang(2009)\u6216 Chang \u7b49\u4eba(Chang et al., 2012) \u6216 Chuang \u7b49\u4eba(Chuang et al., 2014)\u7684\u76f8\u95dc\u8cc7\u8a0a\u64f7\u53d6\u65b9\u6cd5\u90fd\u50c5\u80fd\u5f9e\u591a\u7b46\u5730\u5740\u7db2\u9801\u64f7\u53d6\u8cc7\u8a0a\uff0c Su, 2012) (Chuang et al., 2014)\u4e4b\u7814\u7a76\uff0c\u7d93\u7531\u722c\u53d6\u7db2\u9801\u4e0a\u5305\u542b\u5730\u5740\u7684\u5927 \u91cf\u7db2\u9801(\u5305\u62ec Yellow Page \u8207 Surface Web)\u9032\u884c\u5546\u5bb6\u540d\u7a31\u64f7\u53d6\u3002\u5176\u4e2d Yellow Page \u63d0\u4f9b \u4e86\u5927\u91cf\u5546\u5bb6\u540d\u7a31\u4ee5\u53ca\u5730\u5740\u8207\u5546\u5bb6\u7684\u914d\u5c0d\u8cc7\u6599\uff0c\u800c Surface Web \u5247\u5229\u7528 (Chang et al., 2012) Yahoo \u4e5f\u5206\u5225\u5f9e 2005 \u8207 2002 \u5e74\u958b\u59cb\u63d0\u4f9b\u96fb\u5b50\u5730\u5716\u7684\u670d\u52d9\u3002\u800c\u9019\u4e9b\u670d\u52d9\u9700\u8981\u85c9\u7531\u4f7f\u7528\u8005\u7684\u6a19\u8a18 \u7b49\u7fa4\u773e\u5916\u5305\u7684\u65b9\u5f0f\u5efa\u7acb POI \u8cc7\u8a0a\u3002(Dirk & Susanne , 2007)\u7b49\u4eba\u63d0\u51fa\u4e86\u4e00\u500b\u4ee5\u4f4d\u7f6e\u8cc7\u8a0a\u70ba \u57fa\u790e\u7684\u641c\u5c0b\u5f15\u64ce\uff0c\u53ef\u4ee5\u81ea\u52d5\u5f9e\u7db2\u8def\u8cc7\u6e90\u4e2d\u53d6\u5f97\u8207\u7a7a\u9593\u76f8\u95dc\u7684\u6587\u53e5\uff0c\u800c\u5728\u4ed6\u5011\u6700\u8fd1\u7684\u7814\u7a76 \u4e2d (Ahlers, 2013a; 2013b)\uff0c\u5247\u5c08\u6ce8\u5728\u5982\u4f55\u5f9e\u6df1\u5ea6\u7db2\u9801\u4f8b\u5982\u9ec3\u9801\u8207 Wikipedia \u64f7\u53d6\u51fa\u4f4d\u7f6e\u547d \u540d\u5be6\u9ad4\u3002\u7531\u65bc\u5730\u5740\u662f POI \u7684\u660e\u78ba\u6307\u6a19\uff0c\u56e0\u6b64 Chuang \u7b49\u4eba(Chuang et al., 2014)\u63d0\u51fa\u4ee5\u5ee3\u5ea6 Li \u7684\u7814\u7a76\u4e2d (Li, 2009)\uff0cLi \u4ee5\u5e8f\u5217\u6a19\u8a18(Sequence Labeling)\u548c CRF \u6a21\u578b\u5c0d\u7f8e\u570b\u5730\u5340\u7684\u82f1\u6587\u5730\u5740\u9032\u884c\u8a13\u7df4\u8207\u6e2c\u8a66\u3002Li \u5229\u7528\u8a72\u5730\u5340\u5730\u5740\u7684\u7279\u6027\u5efa\u7acb\u4e86 14 \u7a2e\u7279\u5fb5\uff0c\u4e26\u4f7f\u7528 BIEO \u6a19\u8a18\u6cd5\uff0c\u5be6\u9a57\u7d50\u679c F-measure \u9054\u5230\u4e86 0.913 \u7684\u6e96\u78ba\u7387\u30022011 \u5e74 Huang \u5ef6\u7e8c\u4e86 Li \u7684\u7814\u7a76 (Chang et al., 2012)\uff0c\u5229\u7528 17 \u7a2e\u53f0\u7063\u5730\u5740\u7279\u5fb5\u548c BIEO \u53ca IO \u5169\u7a2e\u6a19\u8a18 \u6cd5\uff0c\u5176\u4e2d IO \u6a19\u8a18\u6cd5\u56e0\u70ba\u908a\u754c\u5075\u6e2c\u80fd\u529b\u8f03\u5f31\uff0c\u9700\u642d\u914d\u6975\u5927\u5206\u6578\u5b50\u5e8f\u5217(Maximal Scoring Subsequence)\u9032\u884c\u4fee\u6b63\u3002BIEO \u6a19\u8a18\u6cd5\u7684\u5be6\u9a57\u7d50\u679c F-measure \u7d04\u5728 0.96 \u81f3 0.99 \u4e4b\u9593\uff0cIO \u6a19\u8a18\u6cd5\u5247\u5728 0.94 \u81f3 0.96 \u4e4b\u9593\u3002 \u76f8\u95dc\u8cc7\u8a0a\u64f7\u53d6\u662f\u5730\u5740\u64f7\u53d6\u7684\u5ef6\u4f38\u7814\u7a76\uff0c\u76ee\u7684\u662f\u91dd\u5c0d\u5df2\u77e5\u7684\u5730\u5740\u64f7\u53d6\u51fa\u8207\u8a72\u5730\u5740\u6709\u95dc \u7684\u8a0a\u606f\uff0c\u5982\uff1a\u96fb\u8a71\u3001\u7db2\u5740\u3001\u96fb\u5b50\u90f5\u4ef6\u3001\u8a55\u8ad6\u2026\u7b49\u8cc7\u8a0a\u3002\u4e3b\u8981\u7684\u4f5c\u6cd5\u662f\u91dd\u5c0d\u5df2\u7d93\u6210\u529f\u64f7\u53d6 \u51fa\u7684\u5730\u5740\uff0c\u627e\u51fa\u53ef\u80fd\u7684\u4e0a\u4e0b\u908a\u754c\u3001\u5283\u51fa\u8cc7\u6599\u7bc4\u570d\u4f5c\u70ba\u8a72\u5730\u5740\u7684\u76f8\u95dc\u63cf\u8ff0\uff0c\u53ef\u4ee5\u8996\u70ba\u4e00\u7a2e \u6df1\u5ea6\u7db2\u9801\u8cc7\u6599\u64f7\u53d6(Deep Web Data Record Extraction)\u7684\u4e00\u7a2e\u7279\u4f8b\u3002\u5728 Li \u7684\u7814\u7a76\u4e2d\uff0c\u4e3b \u8981\u662f\u628a\u6240\u6709\u5730\u5740\u6240\u5728\u7684\u6587\u5b57\u8449\u7bc0\u9ede(Text Leaf Node)\u7576\u4f5c\u8d77\u9ede\uff0c\u5229\u7528\u9019\u4e9b\u7bc0\u9ede\u8d70\u8a2a\u81f3\u6839 \u7bc0\u9ede\u904e\u7a0b\u4e2d\uff0cHtml Tag \u7684\u8b8a\u5316\u7576\u4f5c\u908a\u754c\u9ede\u3002\u4f46\u662f Li \u7684\u65b9\u6cd5\u5c0d\u65bc\u7db2\u9801\u4e2d\u64c1\u6709\u5169\u7a2e\u4ee5\u4e0a\u7684\u5730 \u5740\u76f8\u95dc\u8cc7\u8a0a\u6392\u7248\u7121\u6cd5\u6709\u6548\u64f7\u53d6\uff0c\u70ba\u4e86\u89e3\u6c7a\u6b64\u554f\u984c\uff0cHuang \u6703\u5148\u91dd\u5c0d\u5404\u5730\u5740\u8def\u5f91\u7684\u76f8\u4f3c\u5ea6 \u4f5c\u51fa\u5206\u985e\uff0c\u518d\u91dd\u5c0d\u5404\u985e\u5be6\u884c Li \u7684\u65b9\u6cd5\u3002\u5728\u6700\u5f8c\u82f1\u6587\u5730\u5740\u76f8\u95dc\u8cc7\u8a0a\u64f7\u53d6\u7684\u5be6\u9a57\u4e2d\uff0cLi \u7684\u76f8 \u95dc\u8cc7\u8a0a\u64f7\u53d6\u7684 F-measure \u9054\u5230\u4e86 0.8689\uff0c\u800c\u52a0\u5165\u4e86 Huang \u7684\u6539\u9032\u5247\u63d0\u6607 0.0233\u3002 2012 \u5e74 Su (Su, 2012)\u767c\u73fe Li \u8207 Huang \u7684\u505a\u6cd5\u904e\u5ea6\u7c21\u5316\u5404\u7b46\u7d00\u9304(Record)\u7684\u7522\u751f\u6a21 \u7248 (Template) \uff0cLi \u8207 Huang \u7684\u505a\u6cd5\u4e2d\uff0c\u53ea\u8981\u6a21\u7248\u4e2d\u6709\u4efb\u4f55\u4e00\u7b46\u9078\u64c7\u6027\u8cc7\u6599 (Optional Data) \uff0c \u5c31\u6703\u767c\u751f\u9023\u9396\u932f\u8aa4\u3002\u70ba\u4e86\u89e3\u6c7a\u6b64\u554f\u984c\uff0cSu \u5c07 2010 \u5e74 Wei Liu \u6240\u63d0\u51fa\u57fa\u65bc\u8996\u89ba (Vision-Based) \u7684\u8cc7\u6599\u7d00\u9304(Data Record)\u64f7\u53d6\u6f14\u7b97\u6cd5\u5957\u7528\u5728\u5730\u5740\u76f8\u95dc\u8cc7\u8a0a\u64f7\u53d6\u7684\u7814\u7a76\u4e2d\uff0c\u4e26\u91cd\u4f5c Li \u7684\u5be6\u9a57\uff0c\u5c07 F-measure \u7531 0.7912 \u63d0\u6607\u81f3 0.9504\u3002 Zhang et al., 2007) (Yao, 2011) (Ling et al., 2012)\uff0c2007 \u5e74 Zhang \u7b49\u4eba\u4ee5\u4eba\u6c11\u65e5\u5831 \u7684\u65b0\u805e\u7576\u4f5c\u8a13\u7df4\u8cc7\u6599\uff0c\u5c07\u6578\u500b CRF \u6a21\u578b\u4e32\u9023 \u8d77\u4f86\u9032\u884c\u8fa8\u8b58\uff0c\u63a1\u7528\u7684\u7279\u5fb5\u6709\uff1a\u662f\u5426\u70ba\u524d\u7d1a\u8f38\u51fa\u7684\u5404\u7a2e\u547d\u540d\u5be6\u9ad4(is Named-Entity)\u3001 \u5e38\u898b\u7684\u7d44\u7e54\u540d\u7a31\u958b\u982d\u3001\u5167\u5bb9\u8207\u7d50\u5c3e\u3001N \u5143\u6587\u6cd5 (N-gram) \u3002\u5728 Zhang \u6240\u505a\u7684\u5be6\u9a57\u4e2d\uff0cF-measure \u9054\u5230 0.9794\u3002 2011 \u5e74 Yao (2011)\u5247\u662f\u5c07\u4e2d\u6587\u7d44\u7e54\u540d\u7a31\u5206\u70ba\u4e09\u6bb5\uff1a\u524d\u7f6e\u8a5e(Prefix words)+\u4e2d\u9593\u8a5e (middle words)+\u8a18\u865f\u8a5e(mark words)(\u4f8b\u5982\uff1a\u4e2d\u570b+\u79fb\u52d5\u901a\u8a0a+\u516c\u53f8)\u4e14\u4e0d\u63a1\u7528\u73fe\u6709 \u7684\u6a21\u578b\uff0c\u4f7f\u7528\u81ea\u884c\u8a2d\u8a08\u7684\u7d71\u8a08\u65b9\u6cd5\uff0c\u8003\u616e\u7d44\u7e54\u540d\u7a31\u7684\u983b\u7387\u3001\u8a5e\u6027\u8207\u9577\u5ea6\uff0c\u914d\u5408\u4ee5\u4e0b\u5047\u8a2d \u9032\u884c\u8a08\u7b97\uff1a\u300c\u8a18\u865f\u8a5e\u80fd\u5b8c\u5168\u6536\u9304\u300d\u3001\u300c\u524d\u7f6e\u8a5e\u8207\u4e2d\u9593\u8a5e\u70ba\u540d\u8a5e\u3001\u5f62\u5bb9\u8a5e\u3001\u5e8f\u6578\u6216\u4f4d\u7f6e\u2026 \u7b49\u300d\u3001\u300c\u8a18\u865f\u8a5e\u5927\u90e8\u5206\u70ba\u540d\u8a5e\u300d\u548c\u300c\u7d44\u7e54\u540d\u7a31\u5c0f\u65bc\u7b49\u65bc 10 \u500b\u5b57\u300d\uff0c\u6700\u5f8c\u7684\u5be6\u9a57\u4f7f\u7528\u4e86\u4eba \u6797\u80b2\u6698\u8207\u5f35\u5609\u60e0 \u6c11\u7db2\u7684\u8a9e\u6599\u9032\u884c\u8a13\u7df4\uff0c\u4ee5\u4eba\u6c11\u7db2\u3001\u65b0\u83ef\u7db2 \u548c\u5317\u4eac\u90f5\u96fb\u5927\u5b78\u7db2\u7ad9\u9996\u9801\u7684\u65b0\u805e \u7576\u4f5c\u6e2c\u8a66\u8cc7 \u6599\u3002\u5e73\u5747\u6e96\u78ba\u7387\u6700\u9ad8\u9054\u5230 0.959\uff0c\u5e73\u5747\u53ec\u56de\u503c\u5247\u9054\u5230 0.8724\uff0c\u7686\u8d85\u904e\u96b1\u85cf\u99ac\u53ef\u592b\u6a21\u578b (HMM) \u8207\u6700\u5927\u71b5\u6a21\u578b(ME)\u3002 2012 \u5e74 Ling \u7b49\u4eba (Ling et al., 2012) \u4ee5\u898f\u5247\u5f0f\u7684\u8fa8\u8a8d\u65b9\u6cd5 (Rule-based Named-Entity Recognition)\u8fa8\u8b58\u4eba\u6c11\u65e5\u5831\u8207\u65b0\u6d6a\u7db2\u7684\u65b0\u805e\uff0cLing \u9996\u5148\u5c07\u8a9e\u6599\u7d93\u904e\u65b7\u8a5e\u4e26\u5c07\u4e2d\u6587\u7d44\u7e54\u540d \u7a31\u62c6\u89e3\u70ba\u591a\u500b\u4fee\u98fe\u8a5e(Modifiers)+\u6838\u5fc3\u7279\u5fb5\u8a5e(Core Feature Word)\u3002\u5728\u7d71\u8a08\u8a13\u7df4\u8cc7\u6599 \u5f8c\uff0c\u627e\u51fa\u5e38\u7528\u7684\u6838\u5fc3\u7279\u5fb5\u8a5e\uff0c\u5efa\u7acb\u6838\u5fc3\u7279\u5fb5\u8a5e\u5eab\u7576\u4f5c\u7d44\u7e54\u540d\u7a31\u7684\u7d50\u5c3e\uff0c\u4e26\u627e\u51fa 6 \u7a2e\u5de6\u908a \u754c\u7279\u5fb5(left-border features)\u5224\u65b7\u7d44\u7e54\u540d\u7a31\u7684\u8d77\u9ede\u3002\u5728\u53d6\u5f97\u7d44\u7e54\u540d\u7a31\u5019\u9078\u8005\u4e4b\u5f8c\uff0c\u5229\u7528 \u8a72\u7cfb\u7d71\u7684\u5e38\u898b\u932f\u8aa4\u6a21\u5f0f(Debugging Patterns)\u9032\u884c\u4fee\u6b63\u3002\u6700\u5f8c\u7684\u5be6\u9a57\u7d50\u679c\u986f\u793a\uff0cLing \u7684 \u65b9\u6cd5\u7684 F-measure \u6700\u9ad8\u9054\u5230\u4e86 0.8573\u3002 \u5546\u5bb6\u540d\u7a31\u64f7\u53d6\u8207\u5730\u5740\u914d\u5c0d\u7cfb\u7d71 \u672c\u7814\u7a76\u627f\u7e8c (Su, 2012) (Chuang et al., 2014) \u4e4b\u7814\u7a76\u4ee5\u53ca (Chang et al., 2012) \u4e4b\u5730\u5740\u64f7 \u53d6\u7cfb\u7d71\uff0c\u7d93\u7531\u722c\u53d6\u7db2\u9801\u4e0a\u5927\u91cf\u542b\u6709\u5730\u5740\u7684\u7db2\u9801(\u5305\u62ec Yellow Page \u8207 Surface Web)\u9032\u884c \u500b\u5225\u5b8c\u6574\u7db2\u9801\u7684\u81ea\u52d5\u6a19\u8a18\u6d41\u7a0b(\u5de6)\u8207 Snippets \u81ea\u52d5\u6a19\u8a18\u6d41\u7a0b(\u53f3) \u672c\u7814\u7a76\u53e6\u5916\u4ee5\u5546\u5bb6\u540d\u7a31\u7576\u4f5c\u95dc\u9375\u5b57\u6536\u96c6 Google \u641c\u5c0b\u5f15\u64ce\u63d0\u4f9b\u7684 20 \u7b46 Snippets\uff0c\u4e26 \u4ee5\u6240\u6709\u7684\u5df2\u77e5\u5546\u5bb6\u540d\u7a31\u5c0d\u9019\u4e9b Snippets \u4e2d\u7684\u53e5\u5b50\u9032\u884c\u6a19\u8a18\uff0c\u8a66\u5716\u964d\u4f4e\u500b\u5225\u7db2\u9801\u8cc7\u6599\u7684\u8907 \u96dc\u5ea6\u8207\u6a19\u8a18\u4e0d\u5b8c\u6574\u7684\u554f\u984c\u3002\u5982\u5716\u4e00\u53f3\u5716\u6240\u793a\uff0c\u4ee5 Google Snippets \u70ba\u8cc7\u6599\u4f86\u6e90\u7684\u8655\u7406\u6d41\u7a0b\uff0c \u4e3b\u8981\u5dee\u7570\u9ede\u5728\u81ea\u52d5\u6a19\u8a18\u4e0d\u50c5\u53ea\u7528\u55ae\u4e00\u7684\u5546\u5bb6\u540d\u7a31\u4f86\u5354\u52a9\u6a19\u8a18(\u7a31\u4e4b\u70ba UniLabeling)\uff0c\u800c \u662f\u63a1\u7528\u6240\u7528\u5546\u5bb6\u540d\u7a31\u4f86\u9032\u884c\u6a19\u8a18(\u7a31\u4e4b\u70ba FullLabeling)\uff0c\u5176\u9918\u7686\u8207\u4ee5\u6574\u500b\u7db2\u9801\u70ba\u8cc7\u6599\u4f86 \u6e90\u7684\u8655\u7406\u65b9\u5f0f\u76f8\u540c\u3002\u8a13\u7df4\u8cc7\u6599\u8655\u7406\u6d41\u7a0b\u5982\u4e0b\u6240\u8ff0\uff1a Product \u5c6c\u65bc\u670d\u52d9/\u7522\u54c1\u8a5e, e.g. 3C, \u58fd\u53f8, \u51fa\u79df, \u901a\u4fe1 TEX \u4f5c\u70ba\u8f14\u52a9\u5de5\u5177(Hassan & Sleiman, 2013)\uff0cTEX \u662f\u4e00\u500b Deep Web Crawling Tool\uff0c \u53ef\u4ee5\u5c07\u591a\u500b\u7db2\u9801\u7684\u539f\u59cb\u6a94\u6587\u5b57\u5167\u5bb9\u7576\u4f5c\u8f38\u5165(\u4f5c\u8005\u7a31\u70ba TextSet)\uff0c\u900f\u904e\u5c0b\u627e\u5404\u6587\u4ef6\u6240\u64c1 \u6709\u7684\u5171\u4eab\u6a23\u5f0f(Shared Pattern)\u7576\u4f5c\u7d00\u9304\u7684\u5206\u9694\u9ede\uff0c\u7d93\u904e\u53cd\u8986\u5c0b\u627e\u5171\u4eab\u6a23\u5f0f\u8207\u5207\u5272\u5f8c\uff0c \u627e\u51fa\u6700\u5f8c\u7684\u8cc7\u6599\u7bc0\u9ede\u3002\u85c9\u7531 TEX (Hassan & Sleiman, 2013) \u64f7\u53d6\u51fa\u7db2\u9801\u4e2d\u5177\u6709\u540c\u6027\u8cea\u7684 \u8cc7\u6599\u7bc0\u9ede\uff0c\u7576\u6709\u4e00\u5b9a\u6578\u91cf\u7684\u540c\u985e\u7bc0\u9ede\u88ab\u8a8d\u70ba\u662f\u5546\u5bb6\u540d\u7a31\u4e14\u5546\u5bb6\u540d\u7a31\u9577\u5ea6\u4f54\u7bc0\u9ede\u5167\u5bb9\u7684 20 \uff05\u4ee5\u4e0a\u6642\uff0c\u5247\u628a\u540c\u985e\u7684\u975e\u5546\u5bb6\u540d\u7a31\u7bc0\u9ede\u4e5f\u8996\u70ba\u5546\u5bb6\u540d\u7a31\u9032\u884c\u914d\u5c0d\u3002\u8209\u4f8b\u800c\u8a00\uff0c\u5716 2 \u4e2d\u7684 \u300c\u5929\u5929 100 \u526a\u9aee\u300d\u4e26\u6c92\u6709\u88ab CRF \u8fa8\u8b58\u51fa\u4f86\uff0c\u4f46\u662f\u5728\u540c\u7db2\u7ad9\u7684\u5176\u4ed6\u7db2\u9801\u4e2d\uff0c\u6b64\u7bc0\u9ede\u7684\u5167\u5bb9 \u500b\u7db2\u9801\u9032\u884c\u5be6\u9a57\uff0c\u4f46 Detail Pages \u56e0\u70ba\u914d\u5c0d\u65b9\u6cd5\u9700\u53c3\u8003\u591a\u500b\u7db2\u9801\uff0c\u6240\u4ee5\u96a8\u6a5f\u6311\u9078\u4e86 11 \u500b\u7db2\u7ad9\uff0c\u6bcf\u500b\u7db2\u7ad9\u62bd\u53d6 10 \u500b\u7db2\u9801\u3002\u6700\u5f8c\u5c0d\u9019 410 \u500b\u7db2\u9801\u4eba\u5de5\u6a19\u8a18\u4e86 10,457 \u500b\u5546\u5bb6\u540d \u7a31\u7576\u4f5c\u6e2c\u8a66\u8cc7\u6599\u3002\u800c\u8a13\u7df4\u8cc7\u6599\u5247\u96a8\u6a5f\u6311\u9078\u4e86 30,000 \u500b\u8a13\u7df4\u6a23\u672c\uff0c\u5305\u542b 51,775 \u500b\u4ee5\u81ea\u52d5\u6a19 \u8a18\u6cd5\u6a19\u8a18\u7684\u5546\u5bb6\u540d\u7a31\u3002", "content": "
POI\u64f7\u53d6:\u5546\u5bb6\u540d\u7a31\u8fa8\u8b58\u8207\u5730\u5740\u914d\u5c0d\u4e4b\u7814\u7a76 \u6240\u5f97\u8cc7\u8a0a\u6709\u9650\uff0c\u5c0d\u65bc\u55ae\u7b46\u5730\u5740\u7db2\u9801\u7684\u76f8\u95dc\u8cc7\u8a0a\u64f7\u53d6\u4ecd\u5c1a\u7121\u7814\u7a76\u3002 \u672c\u7814\u7a76\u5f9e\u5730\u5740\u64f7\u53d6\u7684\u89d2\u5ea6\u51fa\u767c\u505a\u70ba\u5546\u5bb6\u8fa8\u8b58\u7684\u6a19\u8a18\uff0c\u5229\u7528\u5df2\u6293\u53d6\u5927\u91cf\u5305\u542b\u5730\u5740\u7684\u7db2 3 \u9801\uff0c\u5148\u627e\u51fa\u7db2\u9801\u4e2d\u7684\u5730\u5740\uff0c\u518d\u85c9\u7531\u5730\u5740\u627e\u51fa\u5c0d\u61c9\u7684\u5546\u5bb6\u540d\u7a31\u9032\u884c\u914d\u5c0d\u3002\u63db\u8a00\u4e4b\uff0c\u7d66\u5b9a\u4e00 \u500b\u5df2\u77e5\u5730\u5740\uff0c\u6211\u5011\u5e0c\u671b\u80fd\u900f\u904e\u7db2\u8def\u8cc7\u6599\u64f7\u53d6\u51fa\u8a72\u5730\u9ede\u7684\u540d\u7a31(\u5982\uff1a\u5546\u5bb6\u540d\u7a31\u3001\u653f\u5e9c\u55ae\u4f4d\u2026 \u7b49)\u3002\u8209\u4f8b\u800c\u8a00\uff1a\u7576\u6211\u5011\u5df2\u6709\u5730\u5740\u300c\u65b0\u5317\u5e02\u677f\u6a4b\u5340\u4e2d\u5c71\u8def\u4e8c\u6bb5 88 \u865f 3F\u300d\uff0c\u6211\u5011\u5e0c\u671b\u80fd \u77e5\u9053\u9019\u500b\u5730\u5740\u5c0d\u61c9\u7684\u540d\u7a31\u300c\u5927\u921e\u91ab\u5b78\u7f8e\u5bb9\u8a3a\u6240\u300d\uff0c\u5982\u6b64\u5373\u53ef\u9032\u4e00\u6b65\u85c9\u7531\u5730\u5740\u3001\u540d\u7a31\u4e26\u5229 \u7528\u641c\u5c0b\u5f15\u64ce\u6536\u96c6\u66f4\u591a\u984d\u5916\u5546\u5bb6\u8cc7\u8a0a\u3002\u9019\u4e9b\u984d\u5916\u8cc7\u8a0a\u4e0d\u50c5\u53ef\u4ee5\u6709\u6548\u63d0\u6607\u5730\u5716\u4e0a\u641c\u5c0b\u4e5f\u5c31\u662f \u5730\u7406\u6aa2\u7d22\u7cfb\u7d71 GIS \u7684\u53ec\u56de\u7387\uff0c\u4e5f\u53ef\u63d0\u6607\u5546\u5bb6\u5206\u985e\u7684\u6e96\u78ba\u7387(\u9673\u5b9c\u52e4 \u7b49\uff0c2013)\u3002 \u5728\u8fa8\u8b58\u5546\u5bb6\u540d\u7a31\u7684\u90e8\u5206\uff0c\u672c\u7bc7\u8ad6\u6587\u4f7f\u7528\u4e86\u689d\u4ef6\u96a8\u6a5f\u57df (Conditional Random Field)\u7576 \u4f5c\u5b78\u7fd2\u6f14\u7b97\u6cd5\u3002\u76ee\u524d\u6709\u8a31\u591a\u95dc\u65bc\u4e2d\u6587\u7d44\u7e54\u540d\u7a31\u8fa8\u8a8d\u7684\u7814\u7a76 (Zhang et al., 2007) (Yao, 2o11) (Ling et al., 2012) (Wu et al., 2008)\uff0c\u53ef\u4ee5\u5f9e\u65b0\u805e\u6216\u4e00\u4e9b\u8f03\u6b63\u5f0f\u7684\u6587\u7ae0\u4e2d\u8403\u53d6\u51fa\u7d44\u7e54\u540d\u7a31\uff0c \u4f46\u662f\u4e26\u6c92\u6709\u5617\u8a66\u4ee5\u4e00\u500b CRF-Model \u76f4\u63a5\u5c0d\u5404\u7a2e\u7db2\u7ad9\u4e2d\u7684\u6574\u500b\u7db2\u9801\u5167\u5bb9\u9032\u884c\u4e2d\u6587\u7d44\u7e54\u540d\u7a31 \u8fa8\u8a8d\u3002\u9019\u5169\u8005\u4e4b\u9593\u4e0d\u540c\u8655\u5728\u65bc\u65b0\u805e\u985e\u6587\u7ae0\u5c6c\u65bc\u8f03\u6b63\u5f0f\u7684\u6587\u7ae0\u9ad4\u88c1\uff0c\u56e0\u6b64\u5bb9\u6613\u51fa\u73fe\u884c\u653f\u6a5f \u95dc\u8207\u6b63\u5f0f\u7684\u7d44\u7e54\u540d\u7a31\uff0c\u4f8b\u5982\uff1a\u884c\u653f\u9662\u548c\u7dad\u5fb7\u98df\u54c1\u6709\u9650\u516c\u53f8\uff0c\u4f46\u662f\u6574\u500b\u7db2\u8def\u4e0a\u5546\u5bb6\u7d44\u7e54\u540d \u7a31\u7684\u547d\u540d\u65b9\u5f0f\u50be\u5411\u5247\u4e0d\u540c\uff0c\u4f8b\u5982\uff1a\u543c\u725b\u6392\u3001\u52aa\u54c7\u514b\u5496\u5561\u3001\u963f\u5b24\u7956\u50b3\u83dc\u5305\u8089\u7cbd\u4ed9\u8349\u2026\u7b49\uff0c \u90fd\u662f\u5546\u5bb6\u7d44\u7e54\u540d\u7a31\u3002\u53e6\u5916\uff0c\u4e00\u500b\u5b8c\u6574\u7684\u7db2\u9801\u5167\u5bb9\u6709\u7d50\u69cb\u8207\u975e\u7d50\u69cb\u5316\u7684\u8cc7\u8a0a\u4ea4\u932f\u5448\u73fe\uff0c\u96d6 \u7136\u7d50\u69cb\u5316\u8cc7\u8a0a\u6703\u9020\u6210\u81ea\u7136\u8a9e\u8a00\u6587\u5b57\u5167\u5bb9\u7684\u7834\u788e\uff0c\u4f46\u9019\u4e9b\u7d50\u69cb\u4e5f\u96b1\u542b\u6709\u53ef\u5229\u7528\u7684\u8cc7\u8a0a\u3002 \u70ba\u4e86\u4f7f\u5546\u5bb6\u8fa8\u8b58\u80fd\u4ee5\u6700\u5c11\u4eba\u529b\u9032\u884c\u81ea\u52d5\u5316\u5b78\u7fd2\uff0c\u672c\u7814\u7a76\u4f7f\u7528\u81ea\u52d5\u6a19\u8a18\u65b9\u5f0f\u5efa\u7acb\u8a13\u7df4 \u8cc7\u6599\uff0c\u6211\u5011\u5148\u91dd\u5c0d\u90e8\u4efd\u7684\u9ec3\u9801\u7db2\u7ad9(\u5982 104 \u6c42\u8077\u7db2\u3001\u611b\u8a55\u7db2\u3001\u5de5\u5546\u540d\u9304\u7db2\u7ad9)\u64b0\u5beb Parser \u53d6\u5f97\u5927\u91cf\u5546\u5bb6\u540d\u7a31\u8207\u5730\u5740\u7684\u7d44\u5408\uff0c\u4e26\u4ee5\u9019\u4e9b\u5df2\u7d93\u53d6\u5f97\u7684\u5546\u5bb6\u540d\u7a31\u5c0d\u7db2\u9801\u8a9e\u6599\u9032\u884c\u81ea\u52d5\u6a19 \u8a18\uff0c\u518d\u5229\u7528\u81ea\u52d5\u6a19\u8a18\u5f8c\u7684\u8a9e\u6599\u8a13\u7df4 CRF \u5e8f\u5217\u6a19\u8a18\u6a21\u578b\u3002\u7136\u800c\u4e00\u500b\u5730\u5740\u53ef\u80fd\u51fa\u73fe\u5728\u591a\u500b\u7db2 \u9801\u4e4b\u4e2d\uff0c\u50c5\u53ea\u4ef0\u8cf4\u5176\u4e2d\u4e00\u500b\u7db2\u9801\u4e5f\u6709\u5931\u4e4b\u504f\u9817\u4e4b\u616e\uff0c\u56e0\u6b64\u6211\u5011\u4e5f\u6536\u96c6\u4e86 Google Snippets \u7576\u4f5c\u8a13\u7df4\u8cc7\u6599\u9032\u884c\u6bd4\u8f03\u3002\u672c\u7bc7\u8ad6\u6587\u7684\u7b2c\u4e8c\u500b\u4e3b\u984c\u5247\u662f\u5546\u5bb6\u5730\u5740\u7684\u914d\u5c0d\uff0c\u7531\u65bc\u4e00\u500b\u7db2\u9801\u53ef \u80fd\u5305\u542b\u591a\u500b\u5546\u5bb6\u540d\u7a31\uff0c\u6211\u5011\u5c0d\u7db2\u9801\u4ee5\u7c21\u55ae\u7684\u898f\u5247\u9032\u884c\u5206\u985e\u5f8c\uff0c\u4f7f\u7528\u4e86\u555f\u767c\u5f0f(heuristic) \u7684\u914d\u5c0d\u898f\u5247\uff0c\u5229\u7528\u5404\u985e\u578b\u7684\u7db2\u7ad9\u6240\u5177\u6709\u7684\u8868\u9054\u7279\u6027\uff0c\u5c0d\u5730\u5740\u8207\u5546\u5bb6\u540d\u7a31\u9032\u884c\u914d\u5c0d\u3002 \u672c\u7814\u7a76\u627f\u7e8c (\u4e4b\u5730\u5740\u64f7\u53d6\u6a21\u578b\u64f7\u53d6\u51fa\u4e86\u53ef\u80fd\u542b\u6709\u53f0\u7063\u5730\u5740\u7684\u7db2\u9801\u8207\u5730\u5740\u6e05\u55ae\u3002\u672c\u7bc7\u8ad6\u6587\u4ee5\u5df2\u77e5\u53ef\u80fd\u542b \u6709\u53f0\u7063\u5730\u5740\u7684\u4e2d\u6587\u7db2\u9801\u3001\u6bcf\u7b46\u7db2\u9801\u7684\u5730\u5740\u6e05\u55ae\u3001\u5927\u91cf\u5546\u5bb6\u540d\u7a31\u6e05\u55ae\u4ee5\u53ca\u5df2\u77e5\u7684\u5730\u5740\u8207\u5546 \u5bb6\u540d\u7a31\u914d\u5c0d\u8cc7\u6599\u70ba\u57fa\u790e\uff0c\u63d0\u51fa\u4e86\u4e00\u500b\u5546\u5bb6\u540d\u7a31\u64f7\u53d6\u7cfb\u7d71\uff0c\u65b9\u6cd5\u5206\u70ba\u4e09\u5927\u6b65\u9a5f\uff1a\u5730\u5740\u7db2\u9801 \u7684\u524d\u8655\u7406\u3001\u5546\u5bb6\u540d\u7a31\u547d\u540d\u5be6\u9ad4\u8fa8\u8a8d\u3001\u53ca\u5730\u5740-\u5546\u5bb6\u540d\u7a31\u5339\u914d\u3002\u672c\u7814\u7a76\u5728\u4e09\u500b\u6a21\u578b\u806f\u5408\u6a19 \u8a18\u5546\u5bb6\u540d\u7a31\u7684\u65b9\u5f0f\u4e0b\uff0c\u5730\u5740\u8207\u5546\u5bb6\u540d\u7a31\u7684\u5e73\u5747\u914d\u5c0d\u6b63\u78ba\u7387\u70ba 0.57\u3002 \u672c\u8ad6\u6587\u5171\u6709\u4e94\u500b\u7ae0\u7bc0\uff0c\u7b2c\u4e00\u7bc0\u662f\u7dd2\u8ad6\uff0c\u8aaa\u660e\u7814\u7a76\u52d5\u6a5f\u8207\u80cc\u666f\uff1b\u7b2c\u4e8c\u7bc0\u662f\u76f8\u95dc\u7814\u7a76\uff0c 4 \u6797\u80b2\u6698\u8207\u5f35\u5609\u60e0 \u4ecb\u7d39\u4e2d\u6587\u7d44\u7e54\u540d\u7a31\u8fa8\u8a8d\u548c\u5730\u5740\u76f8\u95dc\u8cc7\u8a0a\u64f7\u53d6\u7684\u76f8\u95dc\u7814\u7a76\u3002\u7b2c\u4e09\u7bc0\u662f\u65b9\u6cd5\uff0c\u6703\u8a73\u7d30\u4ecb\u7d39\u5982 \u4f55\u5c0d\u5730\u5740-\u7db2\u9801\u5206\u985e\u3001\u4e2d\u6587\u7d44\u7e54\u540d\u7a31\u8fa8\u8a8d\u4ee5\u53ca\u5730\u5740\u8207\u5546\u5bb6\u7d44\u7e54\u540d\u7a31\u7684\u914d\u5c0d\u3002\u7b2c\u56db\u7bc0\u662f\u6211\u5011 \u91dd\u5c0d\u73fe\u6709\u7684\u7db2\u9801\u4e2d\uff0c\u4f9d\u64da\u6211\u5011\u7684\u5206\u985e\uff0c\u6bcf\u985e\u96a8\u6a5f\u62bd\u53d6\u7db2\u9801\u9032\u884c\u7684\u5be6\u9a57\u8207\u7d50\u679c\u5206\u6790\u3002\u6700\u5f8c \u662f\u6211\u5011\u7684\u7d50\u8ad6\u4ee5\u53ca\u672a\u4f86\u7684\u5c55\u671b\u3002 2. \u76f8\u95dc\u7814\u7a76 \u64f7\u53d6\u5730\u5740\u76f8\u95dc\u8cc7\u8a0a\u727d\u6d89\u5230\u4e09\u500b\u9818\u57df\uff0c\u8cc7\u8a0a\u64f7\u53d6(Information Extraction)\u3001\u81ea\u7136\u8a9e\u8a00\u8655\u7406 (Natural Language Processing)\u8207\u8cc7\u8a0a\u6aa2\u7d22(Information Retrieval)\u3002\u9019\u4e09\u8005\u5f7c\u6b64\u9593\u4e92\u76f8 \u4ea4\u932f\uff0c\u5f88\u96e3\u7cbe\u78ba\u5207\u5272\u51fa\u5404\u81ea\u6240\u5c6c\u7684\u7bc4\u7587\u3002\u5927\u81f4\u4e0a\u4f86\u8aaa\uff0c\u8cc7\u8a0a\u64f7\u53d6\u4e3b\u8981\u662f\u5f9e\u5404\u7a2e\u7d50\u69cb\u5316\u8cc7 \u6599\u8207\u975e\u7d50\u69cb\u5316\u6587\u5b57\u8403\u53d6\u51fa\u7279\u5b9a\u8cc7\u8a0a\u7684\u65b9\u6cd5\uff0c\u800c\u81ea\u7136\u8a9e\u8a00\u8655\u7406\u5247\u5c6c\u65bc\u4eba\u5de5\u667a\u6167\u9818\u57df\u7684\u4e00\u500b \u5206\u652f\uff0c\u76ee\u7684\u5728\u65bc\u81ea\u52d5\u5316\u7684\u7406\u89e3\u4e26\u8655\u7406\u4eba\u985e\u6240\u4f7f\u7528\u7684\u8a9e\u8a00\u3002\u8cc7\u8a0a\u6aa2\u7d22\u5247\u662f\u5f9e\u5927\u91cf\u8cc7\u6599\u4e2d\u4ee5 \u6a5f\u7387\u7d71\u8a08\u6a21\u578b\u5c0d\u8cc7\u6599\u9032\u884c\u6392\u5e8f(rank)\u3001\u5efa\u7acb\u7d22\u5f15\uff0c\u5feb\u901f\u627e\u51fa\u4f7f\u7528\u8005\u76ee\u6a19\u6587\u4ef6\u7684\u65b9\u6cd5\u3002 \u672c\u7814\u7a76\u76f8\u95dc\u7684\u4e3b\u8981\u6280\u8853\uff0c\u5206\u5225\u70ba\u5982\u4f55\u6709\u6548\u722c\u53d6\u5305\u542b\u5730\u5740\u4e4b\u76ee\u6a19\u7db2\u9801\u3001\u5730\u5740\u76f8\u95dc\u8cc7\u8a0a \u64f7\u53d6\u8207\u547d\u540d\u5be6\u9ad4\u8fa8\u8a8d\u3002\u5730\u5740\u76f8\u95dc\u8cc7\u8a0a\u64f7\u53d6\u662f\u5728\u5f97\u77e5\u5730\u5740\u8cc7\u8a0a\u5f8c\uff0c\u5f9e\u542b\u6709\u5730\u5740\u7684\u7db2\u9801\u4e2d\u64f7 \u53d6\u51fa\u8207\u8a72\u5730\u5740\u76f8\u95dc\u7684\u8cc7\u8a0a\uff0c\u5982\uff1a\u96fb\u8a71\u3001\u7db2\u5740\u3001\u96fb\u5b50\u90f5\u4ef6\u3001\u8a55\u8ad6\u2026\u7b49\u8cc7\u8a0a\u3002\u547d\u540d\u5be6\u9ad4\u8fa8\u8a8d \u5247\u662f\u70ba\u4e86\u8fa8\u8a8d\u6587\u53e5\u6240\u63d0\u5230\u7684\u7279\u5b9a\u7a2e\u985e\u6982\u5ff5\uff0c\u5982\uff1a\u4eba\u540d\u3001\u5730\u540d\u3001\u7d44\u7e54\u540d\u7a31\u3002\u672c\u7ae0\u4e2d\u5c07\u4f9d\u5e8f \u4ecb\u7d39\u9019\u4e9b\u6280\u8853\u7684\u76f8\u95dc\u7814\u7a76\u3002 2.1 \u5305\u542b\u5730\u5740\u7684\u7db2\u9801\u6293\u53d6\u8207\u5730\u7406\u8cc7\u8a0a\u6aa2\u7d22 \u9019\u88e1\u6240\u8b02\u7684\u5730\u7406\u8cc7\u8a0a\u6aa2\u7d22\uff0c\u662f\u5f9e\u7db2\u8def\u4e0a\u722c\u53d6\u5305\u542b\u5730\u9ede\u6216\u5730\u5740\u7684\u7db2\u9801\uff0c\u8403\u53d6\u5730\u7406\u8cc7\u8a0a\u4e26\u5229 \u7528\u6b64\u8cc7\u8a0a\u6392\u5e8f\u8207\u5efa\u7acb\u7d22\u5f15\uff0c\u63d0\u4f9b\u5feb\u901f\u6aa2\u7d22\u7684\u670d\u52d9\u3002\u76ee\u524d\u7684\u641c\u5c0b\u5f15\u64ce\uff0c\u50cf\u662f Google \u548c \u512a\u5148\u641c\u5c0b\u3001\u9ec3\u9801\u722c\u87f2\u8207\u5730\u5740\u6a23\u7248\u67e5\u8a62\u4e09\u7a2e\u7b56\u7565\u722c\u53d6\u542b\u6709\u5730\u5740\u7684\u7db2\u9801\u3002\u5be6\u9a57\u7d50\u679c\u986f\u793a\u96d6\u7136 \u722c\u53d6\u9ec3\u9801\u7db2\u9801\u53ef\u4ee5\u8f03\u5feb\u53d6\u5f97\u5927\u91cf\u5730\u5740\uff0c\u7136\u800c\u5730\u5740\u6a23\u7248\u67e5\u8a62\u53ef\u4ee5\u88dc\u8db3\u9ec3\u9801\u6db5\u84cb\u5ea6\u4e0d\u8db3\u4e4b\u8655\uff0c \u4e5f\u662f\u5efa\u7acb\u5546\u5bb6\u67e5\u8a62\u670d\u52d9\u4e0d\u53ef\u6216\u7f3a\u7684\u65b9\u6cd5\u3002 2.2 \u5730\u5740\u8207\u76f8\u95dc\u8cc7\u8a0a\u64f7\u53d6 \u5730\u5740\u64f7\u53d6\u662f\u56e0\u61c9\u5730\u5740\u8cc7\u8a0a\u6aa2\u7d22\u6240\u7522\u751f\u7684\u9700\u6c42\uff0c\u76ee\u7684\u662f\u5f9e\u7db2\u8def\u4e0a\u5927\u91cf\u7684\u7db2\u9801\u4e2d\uff0c\u64f7\u53d6\u53d6\u51fa \u5730\u5740\u8cc7\u8a0a\uff0c\u5728 2009 \u5e74 POI\u64f7\u53d6:\u5546\u5bb6\u540d\u7a31\u8fa8\u8b58\u8207\u5730\u5740\u914d\u5c0d\u4e4b\u7814\u7a76 5 Li\u3001Huang \u548c Su \u7684\u7814\u7a76\u7686\u5c08\u6ce8\u65bc\u8cc7\u8a0a\u64f7\u53d6\u7684\u6548\u679c\u4e0a\uff0c\u4f46\u5176\u524d\u63d0\u662f\u7db2\u9801\u4e2d\u5b58\u5728\u591a\u500b\u5730 \u5740\u5b57\u4e32\u3002\u82e5\u63d0\u53ca\u5730\u5740\u76f8\u95dc\u8cc7\u8a0a\u7684\u7db2\u9801\u5167\u4e0d\u5b58\u5728\u5730\u5740\u5b57\u4e32\uff0c\u5247\u7121\u6cd5\u5f97\u77e5\u7db2\u9801\u5167\u542b\u8207 POI \u76f8 \u95dc\u7684\u8cc7\u8a0a\uff0c\u66f4\u4e0d\u53ef\u80fd\u6709\u5f8c\u7e8c\u8403\u53d6\u8cc7\u8a0a\u7684\u904e\u7a0b\u3002\u56e0\u6b64\uff0c\u672c\u7814\u7a76\u8a66\u5716\u64f7\u53d6\u51fa\u5730\u5740\u7684\u5546\u5bb6\u7d44\u7e54 \u540d\u7a31\uff0c\u4ee5\u5229\u5f8c\u7e8c\u7684\u76f8\u95dc\u8cc7\u8a0a\u8403\u53d6\u8207\u6aa2\u7d22\u3002 2.3 \u4e2d\u6587\u7d44\u7e54\u547d\u540d\u5be6\u9ad4\u8fa8\u8a8d \u547d\u540d\u5be6\u9ad4\u8fa8\u8a8d\u5c6c\u65bc\u8cc7\u8a0a\u8403\u53d6\u8207\u81ea\u7136\u8a9e\u8a00\u7684\u4e00\u500b\u5171\u540c\u5206\u652f\uff0c\u6b64\u7814\u7a76\u8d77\u56e0\u65bc\u4efb\u4f55\u7cfb\u7d71\u7686\u7121\u6cd5 \u7aae\u8209\u51fa\u6240\u6709\u7684\u8a5e\u5f59\u8207\u4ee3\u8868\u7684\u610f\u7fa9\uff0c\u56e0\u70ba\u518d\u5927\u7684\u8a5e\u5eab\u90fd\u6703\u6709\u6c92\u6536\u9304\u7684\u8a5e\u5f59(OOV word\uff0c Out-of-Vocabulary)\uff0c\u4e14\u540c\u6a23\u7684\u8a5e\u5f59\u5728\u4e0d\u540c\u7684\u5167\u5bb9\u4e2d\u5f88\u53ef\u80fd\u4ee3\u8868\u4e0d\u540c\u7684\u610f\u7fa9\u3002\u76ee\u524d\u7684\u4e3b \u8981\u65b9\u6cd5\u662f\u5229\u7528\u5e8f\u5217\u6a19\u8a18\u914d\u5408\u6a5f\u7387\u7d71\u8a08\u6a21\u578b\u8a08\u7b97\u51fa\u6700\u53ef\u80fd\u7684\u6a19\u8a18\u3002 \u76ee\u524d\u5df2\u7d93\u6709\u8a31\u591a\u4e2d\u6587\u7d44\u7e54\u540d\u7a31\u8fa8\u8a8d\u7684\u7814\u7a76 (\u7136\u800c\u4e0a\u8ff0\u7814\u7a76\u7686\u8457\u91cd\u65b0\u805e\u8a9e\u6599\u4e4b\u547d\u540d\u5be6\u9ad4\u64f7\u53d6\uff0c\u5c0d\u65bc\u975e\u65b0\u805e\u6587\u4ef6\u7684\u4e00\u822c\u7db2\u9801\u64f7\u53d6\u4e26 \u672a\u8457\u58a8\u3002\u4e8b\u5be6\u4e0a\u7db2\u9801\u7684\u81ea\u7531\u5ea6\u4f7f\u5f97\u547d\u540d\u5be6\u9ad4\u64f7\u53d6\u76f8\u5c0d\u8f03\u70ba\u56f0\u96e3\uff0c\u9019\u4e5f\u662f\u672c\u7bc7\u8ad6\u6587\u7684\u6311\u6230 \u4e4b\u8655\u3002 3. \u5546\u5bb6\u540d\u7a31\u64f7\u53d6\u3002\u6211\u5011\u5f9e\u9019\u4e9b\u7db2\u9801\u4e2d\u904e\u6ffe\u51fa\u542b\u6709\u53f0\u7063\u5730\u5740\u7684\u53ef\u7528\u7db2\u9801\uff0c\u9032\u884c\u5546\u5bb6\u540d\u7a31\u64f7\u53d6\uff0c \u4e4b\u5f8c\u5229\u7528\u7db2\u7ad9\u7684\u7279\u6027\u5982\u6e05\u55ae\u7db2\u9801\u3001\u6df1\u5ea6\u8cc7\u8a0a\u7db2\u9801\u3001\u8a3b\u8173\u7db2\u9801\u3001\u53ca\u81ea\u7531\u6587\u5b57\u7db2\u9801\u7b49\u70ba\u6bcf\u4e00 \u500b\u5730\u5740\u914d\u5c0d\u5546\u5bb6\u540d\u7a31\u3002 3.1 \u5546\u5bb6\u540d\u7a31\u8fa8\u8a8d \u672c\u7814\u7a76\u8a66\u5716\u5c0d\u7db2\u9801\u5167\u5bb9\u64f7\u53d6\u51fa\u6240\u6709\u7684\u5546\u5bb6\u540d\u7a31\uff0c\u9019\u88e1\u6240\u6307\u7684\u5546\u5bb6\u540d\u7a31\u6db5\u84cb\u4e86\u5404\u7a2e\u7bc4\u570d\uff1a \u660e\u78ba\u7684\u8208\u8da3\u9ede(POI\uff0cPoint of Interest)\u3001\u5be6\u969b\u7684\u7d44\u7e54\u540d\u7a31\u548c\u7522\u54c1\u7684\u5ee0\u5546\u540d\u7a31\u3002\u76ee\u524d\u5728\u547d \u540d\u5be6\u9ad4\u8fa8\u8a8d\u7684\u9818\u57df\uff0c\u901a\u5e38\u4f7f\u7528\u5e8f\u5217\u6a19\u8a18\u6cd5(Sequence Labeling)\u900f\u904e\u689d\u4ef6\u96a8\u6a5f\u57df(CRF) \u6a21\u578b\u9032\u884c\u8fa8\u8a8d\uff0c\u7136\u800c\u76e3\u7763\u5f0f\u5b78\u7fd2\u9700\u4ef0\u8cf4\u5927\u91cf\u7684\u8a13\u7df4\u8cc7\u6599\uff0c\u70ba\u6e1b\u5c11\u4eba\u5de5\u6a19\u8a18\u7684\u8ca0\u8377\uff0c\u672c\u6587 \u5229\u7528\u5df2\u77e5\u7684\u5546\u5bb6\u540d\u7a31\u5c0d\u7db2\u9801\u5167\u5bb9\u9032\u884c\u81ea\u52d5\u6a19\u8a18\uff0c\u4e26\u4ee5\u6a19\u8a18\u5f8c\u7684\u7db2\u9801\u6587\u5b57\u7576\u4f5c CRF \u7684\u8a13\u7df4 \u8cc7\u6599\u3002\u7576 CRF \u8a13\u7df4\u5b8c\u7562\u5f8c\uff0c\u5373\u53ef\u5c0d\u7db2\u9801\u5167\u5bb9\u9032\u884c\u5546\u5bb6\u540d\u7a31\u8fa8\u8b58\uff0c\u5efa\u7acb\u5546\u5bb6\u540d\u7a31\u6e05\u55ae\u3002\u4e0b \u9762\u5c07\u5206\u5225\u4ecb\u7d39\u672c\u7814\u7a76\u7684\u81ea\u52d5\u6a19\u8a18\u3001\u4ee5\u53ca\u8a13\u7df4\u8cc7\u6599\u7684\u6e96\u5099\u65b9\u5f0f\u3002 \u85c9\u7531 Web \u4e0a\u7684\u9ec3\u9801\u7db2\u7ad9\u6240\u63d0\u4f9b\u7684\u5546\u5bb6\u8cc7\u8a0a\uff0c\u6211\u5011\u53ef\u4ee5\u53d6\u5f97\u300c\u5730\u5740-\u5546\u5bb6\u540d\u7a31\u5c0d\u300d\u6e05 \u55ae\uff0c\u5c0d\u8a13\u7df4\u7db2\u9801\u9032\u884c\u81ea\u52d5\u6a19\u8a18\u3002\u7136\u800c\u7531\u65bc\u7db2\u9801\u7e3d\u6578\u9054 39.6 \u842c\u7b46\uff0c\u800c\u4e0d\u91cd\u8907\u7684\u5546\u5bb6\u540d\u7a31\u7e3d \u6578\u9ad8\u9054 68.8 \u842c\uff0c\u57fa\u65bc\u57f7\u884c\u6642\u9593\u7684\u8003\u91cf\uff0c\u7121\u6cd5\u5c0d\u6240\u6709\u7684\u7db2\u9801\u7684\u6bcf\u500b\u53e5\u5b50\u90fd\u6aa2\u67e5\u662f\u5426\u5b58\u5728\u5df2 \u77e5\u5546\u5bb6\u540d\u7a31\u3002\u56e0\u6b64\uff0c\u6211\u5011\u4ee5\u6bcf\u7b46\u7db2\u9801\u5df2\u77e5\u7684\u5730\u5740\u6e05\u55ae\u4f86\u52a0\u5feb\u6a19\u8a18\u901f\u5ea6\uff1a\u4e5f\u5c31\u662f\u8aaa\uff0c\u7cfb\u7d71 \u53ea\u6703\u4f9d\u64da\u7db2\u9801\u6240\u64c1\u6709\u7684\u5730\u5740\u67e5\u8a62\u5c0d\u61c9\u7684\u5546\u5bb6\u540d\u7a31\uff0c\u4e26\u5c0d\u7db2\u9801\u5167\u5bb9\u6383\u63cf\u9019\u4e9b\u5c0d\u61c9\u7684\u5546\u5bb6\u540d \u7a31\u662f\u5426\u5b58\u5728\uff0c\u82e5\u5b58\u5728\u5c31\u6703\u4ee5\u7279\u6b8a\u7684\u6a19\u7c64(Tag)\u4f86\u6a19\u8a3b\u9019\u4e9b\u5546\u5bb6\u540d\u7a31\u3002\u5716 1 \u5de6\u5716\u5373\u662f\u81ea\u52d5 \u6a19\u8a18\u81ea\u52d5\u7522\u751f\u8a13\u7df4\u8cc7\u6599\u7684\u6d41\u7a0b\u5716\u3002 POI\u64f7\u53d6:\u5546\u5bb6\u540d\u7a31\u8fa8\u8b58\u8207\u5730\u5740\u914d\u5c0d\u4e4b\u7814\u7a76 7 \u5716 1. \uf0b7 \u524d\u8655\u7406 \u91dd\u5c0d\u6bcf\u4e00\u500b\u539f\u59cb\u7db2\u9801\u5f8c\uff0c\u672c\u7cfb\u7d71\u9996\u5148\u4f7f\u7528 Apache Tika (Apache License, 2004)\u5c07\u7db2\u9801\u5167 \u5bb9\u9023\u540c\u6a19\u984c\u64f7\u53d6\u6210\u6587\u5b57\u5167\u5bb9\u5f8c\u624d\u9032\u884c\u5f8c\u7e8c\u6b65\u9a5f\u3002\u70ba\u4e86\u4f7f\u5e8f\u5217\u55ae\u5143(Tokens)\u7279\u5fb5\u7684\u5f37\u5ea6 \u589e\u5f37\uff0c\u7cfb\u7d71\u6703\u5148\u5c07\u6240\u6709\u5168\u5f62\u7b26\u865f\u8f49\u63db\u6210\u534a\u5f62\u7b26\u865f\uff0c\u5713\u5f27\u578b\u7684\u62ec\u865f\u300c(\u3001(\u3001\ufe59\u300d\u7d71\u4e00\u8f49\u6210 \u300c(\u300d\uff0c\u56e0\u70ba\u6b64\u7a2e\u62ec\u865f\u901a\u5e38\u542b\u6709\u88dc\u5145\u8aaa\u660e\u7684\u610f\u7fa9\u3002\u975e\u5713\u5f27\u578b\u7684\u62ec\u865f\u300c[\u3001{\u3001\u3014\u3001\uff5b\u3001\u3008\u3001\u2026\u300d \u7d71\u4e00\u8f49\u6210\u300c[\u300d\uff0c\u56e0\u70ba\u6b64\u7a2e\u62ec\u865f\u901a\u5e38\u5177\u6709\u5f37\u8abf\u7684\u610f\u601d\u3002\u7b2c\u4e8c\u6b65\u662f\u5c07\u63db\u884c\u7b26\u865f\u3001\u5730\u5740\u96fb\u8a71\u3001 \u6642\u9593\u2026\u7b49\u4ee5\u6b63\u898f\u8868\u793a\u6cd5\u53d6\u4ee3\u6210\u7279\u6b8a\u7684\u5e8f\u5217\u55ae\u5143\uff0c\u9019\u4e9b\u53d6\u4ee3\u52d5\u4f5c\u80fd\u6709\u6548\u52a0\u5f37\u908a\u754c\u7279\u5fb5\uff0c\u7e2e \u77ed\u5e8f\u5217\u7684\u9577\u5ea6\uff0c\u63d0\u6607\u8fa8\u8b58\u6548\u679c\u3002 \uf0b7 \u6a23\u672c\u5e8f\u5217 \u5b8c\u6574\u7684\u7db2\u9801\u5167\u5bb9\u8207\u4e00\u822c\u7684\u6587\u7ae0\u76f8\u6bd4\uff0c\u4e0d\u540c\u7684\u5730\u65b9\u5728\u65bc\u7db2\u9801\u6703\u5229\u7528\u7d50\u69cb\u5316\u8cc7\u8a0a\u3001\u6392\u7248\u7b49\u8868 \u9054\u65b9\u5f0f\u5c07\u6587\u5b57\u5167\u5bb9\u50b3\u9054\u7d66\u4f7f\u7528\u8005\uff0c\u56e0\u6b64\u5f88\u5c11\u6709\u5b8c\u6574\u7684\u53e5\u5b50\uff0c\u800c\u662f\u76f4\u63a5\u628a\u9805\u76ee\u3001\u540d\u7a31\u3001\u5c6c \u6027\u2026\u7b49\u8cc7\u8a0a\u4ee5\u5217\u8868\u6216\u4f9d\u5e8f\u5217\u51fa\u7b49\u65b9\u5f0f\u5448\u73fe\u3002\u82e5\u6211\u5011\u63a1\u53d6\u50b3\u7d71\u7684\u53e5\u5b50\u6a23\u672c\u55ae\u5143(Training or Testing Examples)\uff0c\u9032\u884c\u8a13\u7df4\u8207\u6e2c\u8a66\uff0c\u5f88\u96e3\u6709\u597d\u7684\u6210\u679c\u3002\u56e0\u6b64\u6211\u5011\u5c07\u7db2\u9801\u5167\u5bb9\u8f49\u6210\u6587\u5b57 \u5f8c\uff0c\u79fb\u9664\u7a7a\u767d\u985e\u5b57\u5143\u3001\u4ee5\u9023\u7e8c\u4e09\u500b\u63db\u884c\u7b26\u865f\u7576\u4f5c\u5206\u9694\u7b26\u865f(Delimiter)\uff0c\u5c07\u6587\u5b57\u5207\u70ba\u8a31 \u591a\u5340\u584a (Block) \uff0c\u4ee5\u542b\u6709\u5546\u5bb6\u540d\u7a31\u7684\u5340\u584a\u52a0\u4e0a\u524d\u5f8c\u5340\u584a\uff0c\u4ee5\u9023\u7e8c\u4e09\u5340\u584a\u70ba\u4e00\u500b\u8a13\u7df4\u6a23\u672c\uff0c \u9019\u6a23\u7684\u597d\u8655\u662f\u76e1\u53ef\u80fd\u8b93\u8a13\u7df4\u6a23\u672c\u6db5\u84cb\u5546\u5bb6\u540d\u7a31\uff0c\u4e5f\u80fd\u6709\u8f03\u591a\u7684\u975e\u5546\u5bb6\u540d\u7a31\u7bc4\u4f8b\u3002\u540c\u6a23\u5730 \u5728\u6e2c\u8a66\u6642\uff0c\u4e5f\u63a1\u7528\u4e09\u884c\u6587\u5b57\u70ba\u4e00\u500b\u55ae\u4f4d\u7576\u4f5c\u6a23\u672c\u55ae\u5143\u9032\u884c\u6e2c\u8a66\u3002 8 \u6797\u80b2\u6698\u8207\u5f35\u5609\u60e0 \uf0b7 \u5e8f\u5217\u55ae\u5143\u8207\u6a19\u8a18 \u4e00\u822c\u8aaa\u4f86\uff0c\u5728\u4eba\u540d\u8fa8\u8b58\u4e2d\uff0c\u96d6\u7136\u4eba\u540d\u6709\u5927\u91cf\u7684\u7d44\u5408\u8207\u53ef\u80fd\u6027\uff0c\u4f46\u662f\u4f9d\u7136\u6703\u6709\u6240\u8b02\u7684\u5e38\u7528 \u5b57\uff0c \u300c\u83dc\u5e02\u5834\u540d\u300d\u5c31\u662f\u4e00\u7a2e\u5f88\u597d\u7684\u4f8b\u5b50\u3002\u4f46\u662f\u5546\u5bb6\u7d44\u7e54\u540d\u7a31\u4e2d\u9664\u4e86\u7d50\u5c3e\u90e8\u4efd\u7684\u5e38\u7528\u8a5e\u5916\uff0c \u5728\u4e3b\u8981\u540d\u7a31\u4e0a\u5e7e\u4e4e\u6c92\u6709\u4efb\u4f55\u898f\u7bc4\uff0c\u4f8b\u5982\uff1a\u300c\u571f\u5730\u300d\u3001\u300c\u963f\u5b24\u7956\u50b3\u83dc\u5305\u8089\u7cbd\u4ed9\u8349\u300d\u4e2d\u6240\u6709 \u8a5e\u7686\u70ba\u5e38\u7528\u8a5e\u5f59\uff0c\u300c18 \u5ea6 c \u5de7\u514b\u529b\u5de5\u574a\u300d\u3001\u300c591 \u79df\u5c4b\u300d\u70ba\u4e2d\u82f1\u6578\u5b57\u5143\u4ea4\u932f\u51fa\u73fe\uff0c\u300c\u52aa \u54c7\u514b\u5496\u5561\u300d\u3001\u300c\u857e\u514b\u723e\u70d8\u57f9\u574a\u300d\u70ba\u97f3\u8b6f\u8a5e\uff0c\u300c\u8606\u8588\u82b1\u5712\u96f2\u5357\u98df\u5e9c\u300d\u3001\u300c\u4e09\u5cfd\u6b77\u53f2\u6587\u7269\u9928\u300d \u70ba\u5730\u540d\u3002\u50c5\u7ba1\u5982\u6b64\uff0c\u9019\u4e9b\u5546\u5bb6\u7d44\u7e54\u540d\u7a31\u7684\u8a5e\u6027\u537b\u6709\u5e38\u898b\u5e8f\u5217\uff0c\u5982\u540d\u8a5e+\u540d\u8a5e\u6216\u52d5\u8a5e\u3001\u5c08\u6709 \u540d\u8a5e+\u540d\u8a5e\u6216\u52d5\u8a5e\u3001\u6578\u5b57\u6216\u82f1\u6587+\u540d\u8a5e\u6216\u52d5\u8a5e \u2026\u7b49\uff0c\u6240\u4ee5\u8a5e\u6027\u662f\u4e00\u7a2e\u4e0d\u53ef\u5ffd\u7565\u7684\u91cd\u8981\u7279 \u5fb5\u3002\u56e0\u6b64\u5728\u5e8f\u5217\u55ae\u5143(Tokens)\u7684\u9078\u64c7\u4e0a\uff0c\u6211\u5011\u5229\u7528 Stanford Segmenter \u53ca POS Tagger \u5c07\u7db2\u9801\u7684\u6587\u5b57\u5167\u5bb9\u7d93\u904e\u65b7\u8a5e\u53ca\u8a5e\u6027(POS\uff0cPart of Speech)\u6a19\u8a18\uff0c\u4ee5\u8a5e\u70ba\u55ae\u4f4d\u9032\u884c\u8a13\u7df4 \u8207\u6e2c\u8a66\u3002\u7d93\u904e\u65b7\u8a5e\u7684\u5e8f\u5217\uff0c\u518d\u4ee5 B\u3001I\u3001E\u3001O \u56db\u7a2e\u6a19\u8a18\u4ee3\u8868\u5546\u5bb6\u540d\u7a31\u7684\u8d77\u59cb\u3001\u4e2d\u9593\u3001\u7d50 \u5c3e\u3001\u4ee5\u53ca\u975e\u5546\u5bb6\u540d\u7a31\u3002 \uf0b7 \u7279\u5fb5 \u4e00\u822c\u4eba\u5728\u5224\u65b7\u4e00\u6bb5\u6587\u5b57\u662f\u5426\u662f\u5546\u5bb6\u540d\u7a31\u6642\uff0c\u6703\u4f9d\u9760\u5169\u985e\u7279\u5fb5\uff0c\u7b2c\u4e00\u7a2e\u662f\u5916\u90e8\u7279\u5fb5 (Outside Feature)\uff0c\u9019\u7a2e\u7279\u5fb5\u843d\u5728\u5546\u5bb6\u540d\u7a31\u7684\u5de6\u53f3\uff0c\u4f46\u662f\u6b64\u7a2e\u7279\u5fb5\u7121\u6cd5\u6e96\u78ba\u5224\u65b7\u5546\u5bb6\u540d\u7a31\uff0c\u53ea\u80fd \u9032\u884c\u63a8\u6e2c\u4e0a\u7684\u8f14\u52a9\u3002\u7b2c\u4e8c\u7a2e\u5247\u662f\u5167\u90e8\u7279\u5fb5(Inside Feature)\uff0c\u5167\u90e8\u7279\u5fb5\u80fd\u63d0\u4f9b\u5f37\u70c8\u7684\u5224 \u65b7\u8cc7\u8a0a\uff0c\u56e0\u70ba\u7d55\u5927\u591a\u6578\u7684\u5546\u5bb6\u540d\u7a31\u90fd\u662f\u7531\u4e09\u500b\u90e8\u4efd\u6240\u7d44\u6210\uff1a\u771f\u540d(Real Name)\u3001\u7522\u54c1\u6216 \u670d\u52d9(Service or Product)\u3001\u5730\u6a19\u6027\u8a5e\u5f59(Landmark)\uff0c\u8209\u4f8b\u4f86\u8aaa\uff1a\u300c\u71e6\u5764 3C \u91cf\u8ca9\u5e97\u300d \u53ef\u4ee5\u62c6\u6210\u300c\u71e6\u5764/3C/\u91cf\u8ca9\u5e97\u300d\u6216\u300c\u71e6\u5764/3C \u91cf\u8ca9/\u5e97\u300d\u3002\u5373\u4f7f\u662f\u975e\u5e38\u77ed\u7684\u5546\u5bb6\u540d\u7a31\u90fd\u6703\u6709 \u9019\u7a2e\u7d50\u69cb\uff0c\u4f8b\u5982\uff1a\u300c\u9e97\u5b30\u623f\u300d\u662f\u300c\u9e97/\u5b30/\u623f\u300d\uff0c\u5176\u4e2d\u5b30\u662f\u6307\u63d0\u4f9b\u5152\u7ae5\u7528\u54c1\u3002 \u6211\u5011\u7d71\u8a08\u5df2\u77e5\u7684\u5546\u5bb6\u540d\u7a31\uff0c\u5c0d\u7d44\u7e54\u3001\u5efa\u7bc9\u3001\u623f\u9593\u3001\u5730\u6a19\u5efa\u7acb\u6e05\u55ae\uff0c\u4f8b\u5982\uff1a\u6703\u3001\u57ce\u3001 \u623f\u3001\u7ad9\u2026\u7b49\uff0c\u7576\u6bcf\u500b\u5e8f\u5217\u55ae\u5143(Tokens)\u662f\u4ee5\u6b64\u6e05\u55ae\u4e2d\u7684\u6587\u5b57\u70ba\u7d50\u5c3e\uff0c\u5c31\u8868\u793a\u5177\u6709\u5730\u6a19 \u6027\u8a5e\u5f59\u7684\u7279\u5fb5(Landmark Feature)\u3002\u53e6\u5916\uff0c\u6211\u5011\u4e5f\u6536\u96c6\u4e86\u9ec3\u9801\u7db2\u7ad9\u7684\u670d\u52d9\u3001\u7522\u54c1\u5efa\u7acb\u6e05 \u55ae \uff0c \u5982 \u679c \u5e8f \u5217 \u55ae \u5143 ( Tokens ) \u542b \u6709 \u6b64 \u6e05 \u55ae \u4e2d \u7684 \u8a5e \u5f59 \uff0c \u5c31 \u8868 \u793a \u5177 \u6709 \u7522 \u54c1 \u670d \u52d9 \u7279 \u5fb5 (Service/Product Feature)\u3002 \u7576\u6211\u5011\u6709\u4e86\u4e0a\u8ff0\u5169\u7a2e\u7279\u5fb5\uff0c\u554f\u984c\u5c31\u7c21\u5316\u6210\u5982\u4f55\u627e\u51fa\u771f\u540d(Real Name)\u7684\u90e8\u4efd\uff0c\u7db2\u9801 \u5167\u5bb9\u8207\u4e00\u822c\u6587\u7ae0\u4e0d\u540c\u7684\u5730\u65b9\u5728\u65bc\u540d\u7a31\u66f4\u50be\u5411\u65bc\u55ae\u7368\u51fa\u73fe\uff0c\u800c\u9bae\u5c11\u5b58\u5728\u65bc\u4e00\u6bb5\u5b8c\u6574\u7684\u53e5\u5b50 \u4e2d \uff0c\u6240\u4ee5\u4e00\u6bb5\u6587\u5b57\u610f\u601d\u7684\u8d77\u9ede\u5c31\u8b8a\u6210\u5f88\u91cd\u8981\u7684\u7279\u5fb5\uff1a\u5982\u679c\u4e00\u500b\u5e8f\u5217\u55ae\u5143(Tokens)\u662f\u6a23 \u672c\u55ae\u5143\u7684\u8d77\u9ede\u6216\u524d\u4e00\u500b\u5e8f\u5217\u55ae\u5143\u5c6c\u65bc\u7b26\u865f\u985e\uff0c\u5c31\u5177\u6709\u958b\u59cb\u7279\u5fb5(Start Feature)\uff0c\u53cd\u904e\u4f86 \u8aaa\uff0c\u7576\u5e8f\u5217\u55ae\u5143\u662f\u6a23\u672c\u55ae\u5143\u7684\u7d50\u5c3e\u6216\u4e0b\u4e00\u500b\u5e8f\u5217\u55ae\u5143\u5c6c\u65bc\u7b26\u865f\u985e\uff0c\u5c31\u5177\u6709\u7d50\u5c3e\u7279\u5fb5(End Feature)\u3002\u4f8b\u5982\uff1a\u7db2\u9801\u4e2d\u7684\u6a19\u8a9e\u300c[\u963f\u5b24\u7956\u50b3\u83dc\u5305\u8089\u7cbd\u4ed9\u8349]\u6709\u963f\u5b24\u7684\u7cbe\u795e\u50b3\u627f\u88fd\u4f5c\u51fa\u5ba2 \u5bb6\u50b3\u7d71\u7c73\u98df\u597d\u6ecb\u5473!\u300d\u8207\u7db2\u9801\u6a19\u984c\u300c\u963f\u5b24\u7956\u50b3\u83dc\u5305\u8089\u7cbd\u4ed9\u8349\u300d\u4e2d\uff0c\u524d\u8005\u7684\u300c\u963f\u5b24\u300d\u7684\u524d\u4e00 \u500b\u554f\u984c\u3002 \u5fb5\u5217\u65bc\u8868 1\u3002 Tree Path)\uff0c\u6211\u5011\u53ef\u4ee5\u900f\u904e\u6b64\u7279\u6027\u9032\u884c\u5546\u5bb6\u540d\u7a31\u7684\u4fee\u6b63\u3002 \u78ba\u7b54\u6848\u7684\u90e8\u4efd\uff0c\u5247\u7d66 0~1 \u4e4b\u9593\u7684\u5206\u6578\uff0c\u4e26\u4f9d\u6b64\u5206\u6578\u8a08\u7b97 Precision\u3001Recall\u3001F-measure\uff1a \u5316\u6027\u6975\u5927\u7684\u547d\u540d\u5be6\u9ad4\uff0c\u8f03\u96e3\u8fa8\u8b58\u51fa\u6b63\u78ba\u7684\u7b54\u6848\uff0c\u56e0\u6b64\u9700\u8981\u66f4\u591a\u7684\u7279\u5fb5\u8207\u63d0\u6607\u6a19\u8a18\u54c1\u8cea\u3002 \u6210 O)\u3002\u56e0\u6b64\u5728 Search Snippets \u5be6\u9a57\u4e2d\uff0c\u6211\u5011\u5617\u8a66\u63a2\u8a0e\u964d\u4f4e\u8a9e\u6599\u8907\u96dc\u5ea6\u8207\u6a19\u8a18\u4e0d\u5b8c\u5168\u5169 \u5fb5\u3002\u7cfb\u7d71\u6700\u5f8c\u9078\u64c7\u5c0d\u5546\u5bb6\u540d\u7a31\u5177\u6709\u5f37\u70c8\u5224\u65b7\u8cc7\u8a0a\u7684\u5167\u90e8\u7279\u5fb5\u52a0\u5165\u8a13\u7df4\u6a21\u578b\uff0c\u6240\u6709\u539f\u59cb\u7279 \u7576\u6211\u5011\u5f9e\u591a\u500b\u7db2\u9801\u4f86\u770b\u6642\uff0c\u5730\u5740\u548c\u5546\u5bb6\u540d\u7a31\u901a\u5e38\u64c1\u6709\u540c\u6a23\u7684\u6587\u4ef6\u7269\u4ef6\u6a39\u8def\u5f91(DOM \u8cc7\u8a0a\u6aa2\u7d22\uff0c\u56e0\u6b64\u7cfb\u7d71\u6a19\u8a18\u7d50\u679c\u82e5\u80fd\u5305\u542b\u6b63\u78ba\u7b54\u6848(Gold)\uff0c\u6211\u5011\u5373\u8a8d\u5b9a\u6b63\u78ba\uff0c\u82e5\u662f\u50c5\u70ba\u6b63 \u6599\u4f86\u6e90\u518d\u4ee5\u81ea\u52d5\u6a19\u8a18\u9032\u884c\u8a13\u7df4\uff0c\u53ef\u80fd\u9020\u6210\u8a13\u7df4\u6a23\u672c\u7684\u54c1\u8cea\u4e0d\u4f73\uff0c\u56e0\u6b64\u5c0d\u5546\u5bb6\u540d\u7a31\u9019\u7a2e\u8b8a \u6c92\u6709\u4f7f\u7528\u6240\u6709\u5df2\u77e5\u7684\u5546\u5bb6\u540d\u7a31\u9032\u884c\u6a19\u8a18\uff0c\u6240\u4ee5\u9020\u6210\u4e86\u5927\u91cf\u6a19\u8a18\u932f\u8aa4(\u61c9\u70ba B/I/E/S\u3001\u537b\u6a19 \u5e8f\u5217\u55ae\u5143\u70ba\u7b26\u865f\uff0c\u5f8c\u8005\u70ba\u5e8f\u5217\u55ae\u5143\u7684\u8d77\u9ede\u6240\u4ee5\u7686\u5177\u6709\u958b\u59cb\u7279\u5fb5\uff0c\u300c\u4ed9\u8349\u300d\u5247\u5177\u6709\u7d50\u5c3e\u7279 POI\u64f7\u53d6:\u5546\u5bb6\u540d\u7a31\u8fa8\u8b58\u8207\u5730\u5740\u914d\u5c0d\u4e4b\u7814\u7a76 9 \u8868 1. \u672c\u7814\u7a76\u6240\u4f7f\u7528\u7684\u539f\u59cb\u7279\u5fb5 NO. Feature Explanation 1 Token \u500b\u5225\u8a5e Individual Word, e.g. 591, \u79df\u5c4b 2 isPOS \u8a5e\u6027 Part of Speech, e.g. NR, NN, CD 3 isStart \u6a23\u672c\u5e8f\u5217\u958b\u982d \u6216 \u77ed\u8a9e\u958b\u982d 4 isSymbol \u5c6c\u65bc\u7b26\u865f\u8a5e, e.g. (, [, breakline, !, : 5 isService/6 isLandmark \u5c6c\u65bc\u5730\u6a19\u8a5e, e.g. \u5edf, \u838a, \u516c\u53f8, \u5e97 7 isEnd \u6a23\u672c\u5e8f\u5217\u7d50\u5c3e \u6216 \u77ed\u8a9e\u7d50\u5c3e 3.2 \u5730\u5740-\u5546\u5bb6\u540d\u7a31\u5339\u914d \u7576\u6211\u5011\u6709\u4e86\u5730\u5740\u8207\u5546\u5bb6\u540d\u7a31\u5f8c\uff0c\u4fbf\u53ef\u4ee5\u958b\u59cb\u9032\u884c\u914d\u5c0d\u3002\u7531\u65bc\u5404\u985e\u5225\u7684\u7db2\u9801\u7279\u6027\u5dee\u7570\u5f88\u5927\uff0c \u6240\u4ee5\u7cfb\u7d71\u6703\u91dd\u5c0d\u5404\u985e\u5225\u8a2d\u8a08\u5404\u81ea\u7684\u555f\u767c\u5f0f(heuristic)\u7684\u914d\u5c0d\u65b9\u5f0f\u3002\u9996\u5148\u6211\u5011\u4f9d\u7167\u7db2\u7ad9\u5c07 \u7db2\u9801\u5206\u6210\u4e0d\u540c\u7fa4\u7d44\uff0c\u63a5\u8457\u4f9d\u7db2\u7ad9\u4e2d\u7684\u5730\u5740\u8cc7\u8a0a\u5c07\u7db2\u9801\u5206\u6210\u56db\u985e\u3002 \uf06c \u81ea\u7136\u8a9e\u8a00\u7db2\u9801\uff1a\u7576\u5730\u5740\u5b57\u4e32\u6240\u5728\u7684\u6587\u5b57\u7bc0\u9ede(Text Node)\u6709\u8d85\u904e 50 \u500b\u5b57\u5c31\u6703\u6b78\u985e \u81f3\u81ea\u7136\u8a9e\u8a00\u7db2\u9801 (\u8acb\u53c3\u8003\u5716 2) \uff0c\u56e0\u70ba\u6703\u9019\u500b\u9577\u5ea6\u76f8\u7576\u65bc\u4e00\u5c0f\u584a\u7247\u6bb5\u6587\u5b57 (Snippet) \u3002 \u4f4d\u65bc\u6b64\u7a2e\u7db2\u9801\u7684\u5546\u5bb6\u540d\u7a31\u5de6\u53f3\u5927\u591a\u63a5\u6709\u80fd\u610f\u6703\u5230\u8a72\u8655\u70ba\u5546\u5bb6\u540d\u7a31\u7684\u8a0a\u606f\uff0c\u4f8b\u5982\uff1a \u300c\u8d70 \u9032 edia cafa \u5e97\u88e1\u4e00\u773c\u671b\u53bb\u300d\u3001\u300c\u6211\u6628\u5929\u53bb\u4e86\u71e6\u5764 3C \u8cb7\u6771\u897f\u300d\u3002\u9019\u4e5f\u662f\u7db2\u9801\u4e2d\u552f\u4e00 \u63a5\u8fd1\u4e00\u822c\u6587\u7ae0\u7684\u985e\u5225\u3002\u901a\u5e38\u5177\u6709\u5916\u90e8\u7279\u5fb5 (Outside Feature)\u3002 \uf06c \u8a3b\u8173\u8cc7\u8a0a\u7db2\u9801\uff1a\u7576\u4e00\u500b\u300c\u7db2\u7ad9\u300d\u5167\u8d85\u904e 80%\u7684\u7db2\u9801\u90fd\u6709\u76f8\u540c\u7684\u5730\u5740\u8207\u6587\u4ef6\u7269\u4ef6\u6a39\u8def \u5f91(DOM Tree Path)\uff0c\u9019\u4e9b\u5730\u5740\u5c31\u6703\u6b78\u985e\u81f3\u8a3b\u8173\u8cc7\u8a0a\u7db2\u9801\u3002\u6b64\u985e\u5225\u4e2d\u7684\u6240\u6709\u7db2\u7ad9\uff0c \u5546\u5bb6\u540d\u7a31\u5468\u570d\u7684\u6587\u5b57\u8cc7\u8a0a\u90fd\u6709\u5f88\u9ad8\u7684\u76f8\u4f3c\u5ea6\uff0c\u7d93\u5e38\u6703\u6709\uff1a\u300c\u672c\u7db2\u7ad9\u70ba\u2026\u300d\u300c\u2026\u7248\u6b0a \u6240\u6709\u300d\u3001\u00ae\u3001\u00a9\u3001\u5730\u5740\u3001\u96fb\u8a71\u2026\uff0c\u9019\u4e9b\u8cc7\u8a0a\u5728 N \u5143\u6587\u6cd5(N-Gram)\u7684\u7279\u5fb5\u4e0a\uff0c\u80fd\u63d0 \u4f9b\u6709\u7528\u7684\u8cc7\u8a0a\u3002 \uf06c \u6e05\u55ae\u7db2\u9801\uff1a\u7576\u4e00\u500b\u7db2\u9801\u5167\u5305\u542b\u8d85\u904e 3 \u7b46\u5730\u5740\u6709\u76f8\u540c\u7684\u6587\u4ef6\u7269\u4ef6\u6a39\u8def\u5f91(DOM Tree Path) \uff0c\u9019\u4e9b\u5730\u5740\u5c31\u6b78\u985e\u70ba\u6e05\u55ae\u985e\u578b\u3002\u6e05\u55ae\u578b\u7684\u5546\u5bb6\u540d\u7a31\u96d6\u7136\u4e0d\u50cf\u81ea\u7136\u8a9e\u8a00\u7db2\u9801\u4e2d\uff0c \u5546\u5bb6\u540d\u7a31\u7684\u5de6\u53f3\u5177\u6709\u63cf\u8ff0\u6027\u7684\u6587\u5b57\uff0c\u4f46\u53d6\u800c\u4ee3\u4e4b\u7684\u662f\u5468\u570d\u5177\u6709\u63db\u884c\u7b26\u865f\u3001\u96fb\u8a71\u3001\u5730 \u5740\u3001\u6642\u9593\u7b49\u8cc7\u8a0a\uff0c\u85c9\u7531\u4e8b\u5148\u7528\u6b63\u898f\u8868\u793a\u6cd5\u53d6\u4ee3\u9019\u4e9b\u5b57\u4e32\u5f8c\uff0c\u4ea6\u80fd\u5229\u7528 N-Gram \u53d6\u5f97 \u6b64\u7279\u6027\u3002 \uf06c \u6df1\u5ea6\u8cc7\u8a0a\u7db2\u9801(Detail Pages)\uff1a\u7576\u4e00\u500b\u7db2\u7ad9\u5167\u4e0d\u540c\u7db2\u9801\u7684\u5730\u5740\u6709\u76f8\u540c\u7684\u6587\u4ef6\u7269\u4ef6\u6a39\u8def \u5f91(DOM Tree Path)\uff0c\u4f46\u662f\u5730\u5740\u5b57\u4e32\u537b\u4e0d\u76f8\u540c\uff0c\u9019\u4e9b\u5730\u5740\u5c31\u6b78\u985e\u70ba\u6df1\u5ea6\u8cc7\u8a0a\u7db2\u9801\uff0c \u6797\u80b2\u6698\u8207\u5f35\u5609\u60e0 (a) (b) (c) (d) \u5716 2. (a) \u81ea\u7136\u8a9e\u8a00\u7db2\u9801\u7bc4\u4f8b (b) \u8a3b\u8173\u7db2\u9801\u7bc4\u4f8b (c) \u6e05\u55ae\u7db2\u9801\u7bc4\u4f8b (d)\u6df1\u5ea6\u7db2\u9801 \u7bc4\u4f8b \u5c0d\u65bc\u7b2c\u4e00\u548c\u7b2c\u4e8c\u985e\u7db2\u9801\u800c\u8a00\uff0c\u5730\u5740\u6240\u5c0d\u61c9\u7684\u5546\u5bb6\u540d\u7a31\u901a\u5e38\u843d\u5728\uff1a\u7db2\u9801\u6a19\u984c\u3001\u5730\u5740\u524d\u3001 \u5730\u5740\u5f8c\u6216\u9ad8\u983b\u5546\u5bb6\u540d\u7a31\u3002\u82e5\u53ea\u6709\u4e00\u500b\u5730\u5740\uff0c\u5247\u7b2c\u4e00\u9806\u4f4d\u662f\u7db2\u9801\u6a19\u984c\u4e2d\u7684\u5546\u5bb6\u540d\u7a31\u3002\u5176\u6b21\uff0c \u4ee5\u9760\u8fd1\u5730\u5740\u7684\u5546\u5bb6\u540d\u7a31\u70ba\u512a\u5148\u914d\u5c0d\u5c0d\u8c61\uff0c\u300c\u5730\u5740\u524d\u300d\u7684\u914d\u5c0d\u65b9\u5f0f\u662f\u5c07\u5730\u5740\u8207\u6240\u5728\u4f4d\u7f6e\u7684 \u524d\u4e94\u884c\u5167\u7684\u5546\u5bb6\u540d\u7a31\u5217\u70ba\u914d\u5c0d\u5019\u9078\u8005\uff0c\u800c\u300c\u5730\u5740\u5f8c\u300d\u5247\u662f\u5c07\u5730\u5740\u8207\u6240\u5728\u4f4d\u7f6e\u7684\u5f8c\u5169\u884c\u5167 \u7684\u5546\u5bb6\u540d\u7a31\u5217\u70ba\u914d\u5c0d\u5019\u9078\u8005\uff0c\u7576\u591a\u500b\u5019\u9078\u8005\u8ddd\u96e2\u76f8\u540c\u6642\uff0c\u6703\u4ee5\u7db2\u9801\u4e2d\u51fa\u73fe\u8f03\u591a\u6b21\u7684\u5546\u5bb6 \u540d\u7a31\u70ba\u512a\u5148\uff0c\u82e5\u6b21\u6578\u5b8c\u5168\u76f8\u540c\u5247\u9078\u64c7\u4f4d\u65bc\u5730\u5740\u524d\u65b9\u7684\u5546\u5bb6\u540d\u7a31\u3002 \u81f3\u65bc\u7b2c\u4e09\u548c\u7b2c\u56db\u985e\u7db2\u9801\uff0c\u56e0\u70ba\u7db2\u9801\u901a\u5e38\u7531\u6a21\u677f(Template)\u548c\u7d00\u9304(Record)\u6240\u7d44 \u6210\uff0c\u800c\u76f8\u540c\u985e\u578b\u7684\u7d00\u9304\u6703\u653e\u7f6e\u5728\u985e\u4f3c\u8def\u5f91\u4e0b\uff0c\u6240\u4ee5\u5b58\u5728\u4e00\u500b\u5c08\u9580\u7684\u7814\u7a76\u9818\u57df\u7a31\u70ba Wrapper Induction\uff0c\u76ee\u7684\u662f\u900f\u904e\u53c3\u8003\u4e00\u500b\u6216\u591a\u500b\u7db2\u9801\u5167\u5bb9\u53cd\u5411\u63a8\u5c0e\u51fa\u6a21\u677f\u8207\u7d00\u9304\u3002\u672c\u7814\u7a76\u4e2d\u4f7f\u7528 11 \u300cGM \u9020\u578b\u9928\u300d\u3001\u300c\u80af\u7279\u9020\u578b\u6c99\u9f8d\u300d\u2026\u7b49\u5df2\u88ab\u6210\u529f\u8fa8\u8b58\u70ba\u5546\u5bb6\u540d\u7a31\uff0c\u6240\u4ee5\u7cfb\u7d71\u4e5f\u6703\u5c07\u300c\u5929 \u5929 100 \u526a\u9aee\u300d\u8996\u70ba\u5546\u5bb6\u540d\u7a31\u3002\u672c\u7cfb\u7d71\u4e2d\uff0c\u9580\u6abb\u503c\u70ba 0.2\uff0c\u5373\u8a72\u7bc0\u9ede\u6709 20%\u4ee5\u4e0a\u7684\u5167\u5bb9\u88ab \u8a8d\u70ba\u662f\u5546\u5bb6\u540d\u7a31\uff0c\u5247\u5176\u9918\u7db2\u9801\u7684\u8a72\u7bc0\u9ede\u4e5f\u6703\u88ab\u8a8d\u70ba\u662f\u5546\u5bb6\u540d\u7a31\u3002 Missed Entity Extracted Entity \u2026 \u7b2c\u4e8c\u7a2e\u8cc7\u6599\u4f86\u6e90\u662f\u4f7f\u7528 Google \u641c\u5c0b\u5f15\u64ce\u6240\u53d6\u5f97\u7684\u7db2\u9801\u5167\u5bb9\u7247\u6bb5(Snippets\uff0c\u8acb\u53c3\u8003 POI\u64f7\u53d6:\u5546\u5bb6\u540d\u7a31\u8fa8\u8b58\u8207\u5730\u5740\u914d\u5c0d\u4e4b\u7814\u7a76 13 , \u6bd4\u5c0d\u5206\u6578 1, \u5305\u542b , \u5305\u542b \u8fa8\u8b58\u51fa\u7684\u6240\u6709\u5546\u5bb6\u540d\u7a31\u8207 \u9032\u884c\u6bd4\u5c0d\u7684\u5206\u6578\u7e3d\u548c \u6240\u8fa8\u8b58\u51fa\u7684\u6240\u6709\u5546\u5bb6\u540d\u7a31\u6578\u91cf \u8fa8\u8b58\u51fa\u7684\u6240\u6709\u5546\u5bb6\u540d\u7a31\u8207 \u9032\u884c\u6bd4\u5c0d\u7684\u5206\u6578\u7e3d\u548c \u4eba\u5de5\u6a19\u8a18\u7684\u6240\u6709\u5546\u5bb6\u540d\u7a31\u6578\u91cf \u6b64\u7a2e\u8a55\u4f30\u65b9\u5f0f\uff0c\u53ef\u4ee5\u89e3\u6c7a\u7576 CRF \u8fa8\u8b58\u51fa\u7684\u5546\u5bb6\u540d\u7a31\u908a\u754c\u5305\u542b\u5730\u540d\u3001\u767e\u5e74\u8001\u5e97\u2026\u7b49\u96e3\u4ee5\u5224 \u5b9a\u662f\u5426\u5c6c\u65bc\u5546\u5bb6\u540d\u7a31\u7684\u4e00\u90e8\u5206\u7684\u554f\u984c\u3002 14 \u6797\u80b2\u6698\u8207\u5f35\u5609\u60e0 TrainSet1 TrainSet2 TrainSet3 TrainSet4 TrainSet5 0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 200,000 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 F1 (Auto Labeling, Score Match) Snippet NER POI\u64f7\u53d6:\u5546\u5bb6\u540d\u7a31\u8fa8\u8b58\u8207\u5730\u5740\u914d\u5c0d\u4e4b\u7814\u7a76 15 \u8868 4. \u4ea4\u53c9\u6e2c\u8a66 Whole Page Search Snippets Whole Page Model 0.305 0.473 Snippets Model (Full Labeling) 0.310 0.791 \u7b2c\u4e09\uff0c\u7576\u6211\u5011\u5229\u7528\u5df2\u77e5\u7684\u5546\u5bb6\u540d\u7a31\u9032\u884c\u6a19\u8a18\u6642\uff0c\u9019\u4e9b\u5df2\u77e5\u8cc7\u6599\u53ef\u80fd\u5b58\u5728\u4e0d\u6b63\u78ba\u3001\u4e0d\u9f4a\u5168 \u6216\u662f\u6b67\u7fa9\u6027\u7b49\u554f\u984c\uff0c\u9020\u6210\u81ea\u52d5\u6a19\u8a18\u7684\u7b2c\u4e00\u6b21\u932f\u8aa4\uff0c\u800c\u4e14\u64c1\u6709\u5927\u91cf\u7684\u5df2\u77e5\u540d\u7a31\u548c\u7db2\u9801\u6642\uff0c \u7121\u6cd5\u5c0d\u6240\u6709\u7db2\u9801\u4e2d\u7684\u6240\u6709\u5b57\u4e32\u90fd\u6aa2\u67e5\u662f\u5426\u5b58\u5728\u5546\u5bb6\u7d44\u7e54\u540d\u7a31\uff0c\u53ea\u80fd\u5229\u7528\u5730\u5740\u67e5\u8a62\u662f\u5426\u5b58 \u5728\u5c0d\u61c9\u7684\u5546\u5bb6\u540d\u7a31\uff0c\u9020\u6210\u7b2c\u4e8c\u6b21\u7684\u932f\u8aa4\uff0c\u82e5\u8981\u5728\u5408\u7406\u7684\u57f7\u884c\u6642\u9593\u5167\u89e3\u6c7a\u6b64\u554f\u984c\uff0c\u53ef\u80fd\u9700 \u8981\u4f7f\u7528 Hadoop \u6216\u662f\u5176\u4ed6\u5206\u6563\u5f0f\u7cfb\u7d71\uff0c\u4ee5\u6240\u6709\u5df2\u77e5\u7684\u5546\u5bb6\u540d\u7a31\u9032\u884c\u6a19\u8a18\u4ee5\u63d0\u6607\u6a19\u8a18\u54c1\u8cea\u3002 16 \u6797\u80b2\u6698\u8207\u5f35\u5609\u60e0 \u4e86 POI\u64f7\u53d6:\u5546\u5bb6\u540d\u7a31\u8fa8\u8b58\u8207\u5730\u5740\u914d\u5c0d\u4e4b\u7814\u7a76 \u5716 3. \u6df1\u5ea6\u8cc7\u6599\u7db2\u9801\u914d\u5c0d\u7bc4\u4f8b \u7576\u6211\u5011\u5229\u7528\u8def\u5f91\u627e\u51fa\u6240\u6709\u53ef\u80fd\u7684\u5546\u5bb6\u540d\u7a31\u5f8c\uff0c\u5c07\u958b\u59cb\u9032\u884c\u5be6\u969b\u914d\u5c0d\u3002\u6e05\u55ae\u578b\u7db2\u9801\u8207 \u6df1\u5ea6\u8cc7\u8a0a\u7db2\u9801\u7684\u914d\u5c0d\u65b9\u5f0f\u5927\u81f4\u76f8\u540c\uff1a\u4ee5\u6bcf\u7b46\u5730\u5740\u7684\u4e0a\u65b9\u5168\u90e8\u5167\u5bb9\u8207\u4e0b\u65b9\u5169\u884c\u5167\u7576\u4f5c\u914d\u5c0d \u5019\u9078\uff0c\u96e2\u5730\u5740\u8fd1\u7684\u512a\u5148\u914d\u5c0d\uff0c\u7576\u8ddd\u96e2\u76f8\u540c\u6642\uff0c\u4ee5\u5730\u5740\u524d\u65b9\u7684\u5546\u5bb6\u540d\u7a31\u70ba\u512a\u5148\u3002\u4f46\u6e05\u55ae\u578b \u7db2\u9801\u6703\u4ee5\u5730\u5740\u70ba\u754c\u7dda\uff0c\u5728\u6311\u9078\u914d\u5c0d\u5019\u9078\u8005\u6642\uff0c\u4e0d\u6703\u8d8a\u904e\u5730\u5740\u9032\u884c\u914d\u5c0d\u3002 \u53e6\u5916\u7576\u6211\u5011\u4ee5\u5730\u5740\u70ba\u95dc\u9375\u5b57\u6536\u96c6 Google Snippet \u5f8c\uff0c\u9019\u4e9b Snippets \u4e2d\u7684\u7db2\u9801\u7d50\u69cb\u8cc7 \u8a0a\u8f03\u5f31\uff0c\u4f46\u662f\u53ef\u4ee5\u540c\u6642\u53c3\u8003\u5927\u91cf\u8207\u8fd1\u671f\u76f8\u95dc\u7684\u7db2\u9801\u63d0\u9ad8\u53ef\u4fe1\u5ea6\uff0c\u6240\u4ee5\u7576\u6211\u5011\u4ee5\u5546\u5bb6\u540d\u7a31 \u7684 Snippet \u8a13\u7df4\u51fa CRF \u6a21\u578b\u5f8c\uff0c\u5c31\u76f4\u63a5\u4ee5\u67d0\u5730\u5740\u70ba\u95dc\u9375\u5b57\u6240\u5f97\u5230\u7684\u6240\u6709 Snippets \u4e2d\uff0c\u51fa \u73fe\u6700\u591a\u6b21\u7684\u5546\u5bb6\u540d\u7a31\u548c\u8a72\u5730\u5740\u9032\u884c\u914d\u5c0d\u3002 4. \u5be6\u9a57 \u56e0\u70ba\u672c\u7814\u7a76\u662f\u5148\u9032\u884c\u5546\u5bb6\u540d\u7a31\u8fa8\u8a8d\uff0c\u518d\u5c07\u5730\u5740\u8207\u5df2\u77e5\u7684\u5546\u5bb6\u540d\u7a31\u9032\u884c\u914d\u5c0d\uff0c\u6240\u4ee5\u5be6\u9a57\u90e8 \u4efd\u4e5f\u4f9d\u7167\u9019\u5169\u500b\u968e\u6bb5\u4f86\u9032\u884c\u3002\u7b2c\u4e00\u968e\u6bb5\u7684\u5be6\u9a57\u70ba\u5546\u5bb6\u540d\u7a31\u8fa8\u8b58\u7387\uff0c\u8cc7\u6599\u4f86\u6e90\u6709\u5169\u7a2e\uff0c\u7b2c \u4e00\u7a2e\u662f\u4ee5[9]\u6240\u53d6\u5f97\u7684\u7d04 50 \u842c\u500b\u53ef\u80fd\u542b\u6709\u5730\u5740\u7684\u7db2\u9801\u7576\u4f5c\u539f\u59cb\u8cc7\u6599\uff0c\u5728\u7d93\u904e\u524d\u8655\u7406\u5f8c\uff0c \u904e\u6ffe\u51fa\u7d04 39 \u842c\u500b\u542b\u6709\u53f0\u7063\u5730\u5740\u7684\u7db2\u9801\uff0c\u7d93\u904e\u5730\u5740\u6b63\u898f\u5316\u5f8c\u542b 19 \u842c\u7b46\u53f0\u7063\u5730\u5740(\u8acb\u53c3\u8003 \u8868 2)\u3002\u5728\u7d93\u904e\u7db2\u9801\u5206\u985e\u5f8c\uff0c\u6211\u5011\u96a8\u6a5f\u6311\u9078\u5404\u985e\u4e2d\u7684 100 \u500b\u7db2\u7ad9\uff0c\u6bcf\u500b\u7db2\u7ad9\u4e2d\u5404\u96a8\u6a5f\u62bd \u53d6 1 \u2026 Node List by TEX \u8868 3)\uff0c\u5728\u8a13\u7df4\u8cc7\u6599\u7684\u90e8\u4efd\uff0c\u6211\u5011\u4ee5 11,138 \u7b46\u5546\u5bb6\u540d\u7a31\u9032\u884c\u67e5\u8a62\uff0c\u4ee5\u81ea\u52d5\u6a19\u8a18\u7684\u65b9\u5f0f\u7522 \u751f\u4e86\u5169\u7a2e\u8a13\u7df4\u8cc7\u6599\uff1aSnippetUniLabeling \u548c SnippetFullLabeling\uff0c\u5728 SnippetsUniLabeling \u4e2d\uff0c\u6211\u5011\u50c5\u4ee5\u95dc\u9375\u5b57\u7684\u5546\u5bb6\u540d\u7a31\u5c0d Snippets \u4e2d\u7684\u53e5\u5b50\u9032\u884c\u6a19\u8a18\uff0c\u5171\u6a19\u8a18\u4e86 222,121 \u500b\u5546 \u5bb6\u540d\u7a31\uff0c\u800c SnippetsFullLabeling \u4e2d\uff0c\u5247\u662f\u4ee5\u6240\u6709\u5df2\u77e5\u7684\u5546\u5bb6\u540d\u7a31\u5c0d Snippets \u4e2d\u6240\u6709\u53e5\u5b50 \u9032\u884c\u6a19\u8a18\uff0c\u5171\u6a19\u8a18\u4e86 390,113 \u500b\u5546\u5bb6\u540d\u7a31\uff0c\u85c9\u7531\u4e0d\u540c\u7684\u6a19\u8a18\u65b9\u5f0f\u7522\u751f\u4e0d\u540c\u7a0b\u5ea6\u7684\u96dc\u8a0a\uff0c \u4ee5\u4e86\u89e3\u96dc\u8a0a\u5c0d\u8fa8\u8b58\u7387\u7684\u5f71\u97ff\u3002\u5728\u6e2c\u8a66\u8cc7\u6599\u7684\u90e8\u4efd\u5247\u4ee5 6,963 \u7b46\u5730\u5740\u70ba\u95dc\u9375\u5b57\uff0c\u6536\u96c6\u6bcf\u7b46 \u5730\u5740\u6392\u540d\u524d 20 \u7684\u641c\u5c0b\u7d50\u679c(Snippets)\uff0c\u4ee5\u81ea\u52d5\u6a19\u8a18\u7684\u7b54\u6848\u9032\u884c\u6700\u5f8c NER \u6548\u80fd\u8a55\u4f30\u3002\u6700 \u5f8c\u518d\u5c0d\u5169\u985e\u8cc7\u6599\u9032\u884c\u4ea4\u53c9\u6e2c\u8a66\u3002 \u8868 3. Training Data Testing Data # of Store Queries Tag Stores # of Address Queries Stores (Auto Labeling) Snippet Uni Labeling 11,138 222,121 6,963 70,449 Snippet Full Labeling 11,138 390,113 6,963 70,449 \u7b2c\u4e8c\u968e\u6bb5\u70ba\u5730\u5740\u8207\u5546\u5bb6\u540d\u7a31\u914d\u5c0d\u7684\u6b63\u78ba\u7387\uff0c\u91dd\u5c0d\u4e0d\u540c\u8cc7\u6599\u4f86\u6e90\u4ee5\u5404\u81ea\u7684\u65b9\u5f0f\u9032\u884c\u914d \u5c0d\uff0c\u7b2c\u4e00\u7a2e\u662f\u91dd\u5c0d\u4e0d\u540c\u7db2\u9801\u985e\u5225\u4ee5\u5404\u81ea\u7684\u555f\u767c\u5f0f(heuristic)\u898f\u5247\u9032\u884c\u914d\u5c0d\uff0c\u7b2c\u4e8c\u7a2e\u662f\u4ee5 Snippets \u4e2d\u5404\u5546\u5bb6\u540d\u7a31\u7684\u6700\u9ad8\u51fa\u73fe\u6b21\u6578\u9032\u884c\u914d\u5c0d\u3002 \u6a19\u8a18\u6bd4\u5c0d\u7684\u8a55\u4f30\u65b9\u5f0f\u5982\u4e0b\uff1a\u96d6\u7136\u6211\u5011\u6709\u660e\u78ba\u8a02\u51fa\u5546\u5bb6\u7d44\u7e54\u540d\u7a31\u7684\u5224\u5b9a\u898f\u5247\uff0c\u4f46\u5f88\u591a \u6642\u5019\u4f9d\u7136\u96e3\u4ee5\u6e96\u78ba\u5b9a\u51fa\u908a\u754c\u6a19\u6e96\uff0c\u4f8b\u5982\uff1a\u300c\u98ef\u5e97\u540d\u7a31\uff1a\u897f\u9580\u661f\u8fb0\u5927\u98ef\u5e97\u300d\u4e2d\uff0c\u300c\u897f\u9580\u300d \u4e8c\u5b57\u8a72\u4e0d\u8a72\u5217\u5165\u5546\u5bb6\u540d\u7a31\u4e2d\u6709\u8a31\u591a\u610f\u898b\u5206\u6b67\u7684\u60c5\u6cc1\uff0c\u7531\u65bc\u5546\u5bb6\u540d\u7a31\u4e3b\u8981\u63d0\u4f9b\u5f8c\u7e8c\u7684\u5730\u7406 Sentences 7000 10000 30000 90000 190000 UniTagStores 10902 15669 45425 115014 222121 \u5716 7. \u4ee5\u5b8c\u6574\u7db2\u9801\u70ba\u8cc7\u6599\u4f86\u6e90\u7684\u914d\u5c0d\u6b63\u78ba\u7387(\u8a13\u7df4\u6a23\u672c\u6578\uff1a4,398) 4.2 \u5730\u5740-\u5546\u5bb6\u540d\u7a31 \u5339\u914d\u6b63\u78ba\u7387 4.1 \u5546\u5bb6\u540d\u7a31\u8fa8\u8b58\u7387 FullTagStores 15289 21642 64379 189803 390113 \u7684\u5546\u5bb6\u540d\u7a31\uff0c\u518d\u4ee5\u5546\u5bb6\u540d\u7a31\u7576\u4f5c\u641c\u5c0b\u5f15\u64ce\u7684\u95dc\u9375\u5b57\u53d6\u5f97 POI \u7684\u76f8\u95dc\u8cc7\u8a0a\uff0c\u5c31\u53ef\u4ee5\u6210\u529f\u5efa \u5716 6 \u662f SnippetFullLabeling \u4ee5\u4e0d\u540c\u8a13\u7df4\u8cc7\u6599\u6240\u8a13\u7df4\u51fa\u7684\u6a21\u578b\u5c0d\u914d\u5c0d\u6b63\u78ba\u7387\u7684\u5f71\u97ff\uff0c\u5716\u4e2d \u6211\u5011\u9996\u5148\u4ee5\u500b\u5225\u5b8c\u6574\u7db2\u9801\u70ba\u8cc7\u6599\u4f86\u6e90\uff0c\u5be6\u9a57\u4e86\u8a13\u7df4\u8cc7\u6599\u6578\u91cf\u5c0d\u8fa8\u8b58\u6548\u80fd\u7684\u5f71\u97ff\u3002\u63a5\u8457\u6211 UniLabel 0.134 0.243 0.176 0.175 0.086 \u7acb POI \u8cc7\u6599\u5eab\u3002 \u986f\u793a\u7576 NER \u7684\u6548\u80fd\u5927\u5e45\u63d0\u9ad8\u6642\uff0cMatch \u96d6\u7136\u8ddf\u8457\u4e0a\u5347\uff0c\u4f46\u50c5\u6709\u5fae\u5e45\u6210\u9577\u3002\u800c\u5728\u5b8c\u6574\u7db2\u9801 \u5011\u4ee5 Snippet \u70ba\u8cc7\u6599\u4f86\u6e90\uff0c\u5206\u5225\u5be6\u9a57\u4e86 Uni-Labeling \u548c Full-Labeling \u7684\u6548\u80fd\uff0c\u4ee5\u4e86\u89e3\u5728\u81ea FullLabel 0.564 0.624 0.679 0.740 0.791 \u70ba\u8cc7\u6599\u7684\u5be6\u9a57\u4e2d\uff0c\u96d6\u7136\u6211\u5011\u7121\u6cd5\u8fa8\u8a8d\u51fa\u6240\u6709\u7684\u5546\u5bb6\u540d\u7a31\uff0c\u4f46\u7d93\u7531\u555f\u767c\u5f0f(heuristic)\u7684\u914d \u904e\u53bb\u547d\u540d\u5be6\u9ad4\u4ee5\u65b0\u805e\u5831\u5c0e\u4e2d\u7684\u4eba\u540d\u3001\u5730\u540d\u3001\u7d44\u7e54\u540d\u64f7\u53d6\u70ba\u4e3b\u8ef8\uff0c\u76ee\u7684\u5728\u4e86\u89e3\u65b0\u805e\u4e2d \u52d5\u6a19\u8a18\u4e2d\uff0c\u96dc\u8a0a\u5c0d\u8fa8\u8b58\u6548\u80fd\u7684\u5f71\u97ff\uff0c\u7136\u5f8c\u5c0d\u5169\u7a2e\u8cc7\u6599\u4f86\u6e90\u6240\u8a13\u7df4\u51fa\u7684\u6a21\u578b\u9032\u884c\u4ea4\u53c9\u6e2c\u8a66\uff0c \u89c0\u5bdf\u4e0d\u540c\u4f86\u6e90\u7684\u8a13\u7df4\u8cc7\u6599\u6240\u8a13\u7df4\u51fa\u7684\u6a21\u578b\uff0c\u61c9\u7528\u5728\u4e0d\u540c\u6e2c\u8a66\u8cc7\u6599\u6642\u7684\u8868\u73fe\u3002\u6700\u5f8c\u662f\u672c\u7814 \u7a76\u7684\u5728\u5546\u5bb6\u8fa8\u8b58\u90e8\u4efd\u7684\u6700\u5f8c\u8f38\u51fa\u3002 \u5716 4. \u5b8c\u6574\u7db2\u9801\u4e2d\uff0c\u8a13\u7df4\u8cc7\u6599\u6578\u91cf\u5c0d F1 \u7684\u5f71\u97ff \u5c0d\u898f\u5247\uff0c\u53ef\u4ee5\u63d0\u6607\u914d\u5c0d\u7684\u6b63\u78ba\u7387\u3002\u5716 7 \u662f\u4ee5\u5b8c\u6574\u7db2\u9801\u70ba\u8cc7\u6599\u4f86\u6e90\uff0c\u5730\u5740-\u5546\u5bb6\u540d\u7a31\u914d\u5c0d\u6b63 \u7684\u4e8b\u4ef6\uff0c\u4f46\u5c0d\u65bc\u7db2\u8def\u4e0a\u7684\u8208\u8da3\u9ede POI \u7684\u6536\u96c6\u8f03\u5c11\u8457\u58a8\u3002\u672c\u7814\u7a76\u8a66\u5716\u76f4\u63a5\u5c0d\u6574\u500b\u7db2\u9801\u9032\u884c \u5716 5. \u4ee5 Snippets \u70ba\u8cc7\u6599\u4f86\u6e90\uff0c\u96dc\u8a0a\u8207\u8a13\u7df4\u8cc7\u6599\u6578\u91cf\u5c0d\u6548\u80fd\u7684\u5f71\u97ff \u78ba\u7387\u7684\u5be6\u9a57\u7d50\u679c\u3002\u4ee5\u55ae\u4e00\u985e\u5225\u4f86\u770b\uff0c\u5728\u6df1\u5ea6\u8cc7\u8a0a\u7db2\u9801\u7684\u5be6\u9a57\u4e2d\uff0c\u5229\u7528\u6587\u4ef6\u7269\u4ef6\u6a39\u8def\u5f91\u7684 \u8fa8\u8a8d\uff0c\u96d6\u7136\u53d7\u9650\u65bc\u6a19\u8a18\u7684\u4e0d\u5168\uff0c\u5728\u547d\u540d\u5be6\u9ad4\u8fa8\u8a8d\u7684\u6548\u679c\u4e26\u4e0d\u597d\uff0c\u4f46\u662f\u5728\u6df1\u5ea6\u8cc7\u8a0a\u7db2\u9801(\u4e5f \u5728 Snippets \u65b9\u9762\u7684\u5be6\u9a57\uff0c\u6211\u5011\u6e2c\u8a66\u4e86\u8a13\u7df4\u8cc7\u6599\u6578\u91cf\u8207\u6a19\u8a18\u54c1\u8cea\u5c0d\u8fa8\u8b58\u6548\u80fd\u7684\u5f71\u97ff\uff0c \u76f8\u4f3c\u5ea6\u5f8c\uff0c\u53ef\u4ee5\u5c07\u914d\u5c0d\u6e96\u78ba\u7387\u63d0\u6607\u81f3 0.951\uff0c\u5e73\u5747\u6b63\u78ba\u7387\u5247\u70ba 0.573\u3002 \u662f\u542b\u6709\u6700\u591a\u5730\u5740\u7684\u7db2\u9801\u985e\u578b)\u7684\u5730\u5740-\u5546\u5bb6\u540d\u7a31\u914d\u5c0d\u4e2d\uff0c\u5229\u7528\u7db2\u9801\u9593\u7684\u76f8\u4f3c\u5ea6\u53ef\u4ee5\u53d6\u5f97 \u4ee5\u4e86\u89e3\u5728\u81ea\u52d5\u6a19\u8a18\u4e2d\uff0c\u96dc\u8a0a\u5c0d\u8fa8\u8b58\u6548\u80fd\u7684\u5f71\u97ff\u3002\u5982\u5716 5 \u6240\u793a\uff0c\u5728 UniLabeling \u6a21\u578b\u4e2d\uff0c\u7576 0.9514 \u7684\u6e96\u78ba\u7387\uff0c\u800c\u5e73\u5747\u6b63\u78ba\u7387\u5247\u70ba 0.5726\u3002\u800c Google Snippets \u7684\u65b9\u6cd5\u4e2d\uff0cNER \u6548\u80fd\u6700 \u8cc7\u6599\u589e\u52a0\u6642\uff0c\u8a13\u7df4\u8cc7\u6599\u542b\u6709\u7684\u96dc\u8a0a (\u6a19\u8a18\u4e0d\u5b8c\u5168) \u66f4\u70ba\u56b4\u91cd\uff0c\u4f7f\u5f97\u6548\u80fd\u4e0b\u964d\uff1b\u800c FullLabeling Sentences, Full Labeling Performance \u9ad8\u70ba 0.791\uff0c\u914d\u5c0d\u6b63\u78ba\u7387\u6700\u9ad8\u70ba 0.632\u3002 \u6a21\u578b\u56e0\u70ba\u4f7f\u7528\u6240\u6709\u7684\u5546\u5bb6\u540d\u7a31\u9032\u884c\u6a19\u8a18\uff0c\u6240\u4ee5\u96dc\u8a0a\u5927\u5e45\u6e1b\u5c11\uff0c\u5728\u8cc7\u6599\u589e\u52a0\u7684\u60c5\u6cc1\u4e0b\u53ef\u5927 \u5e45\u5ea6\u63d0\u6607\u6548\u80fd\uff0cFullLabeling \u6a21\u578b\u7684\u6548\u80fd\u6700\u9ad8\u70ba 0.791\u3002 \u4e0d\u904e\u5728 Search Snippets \u7684\u6e2c\u8a66\u8cc7\u6599\u4e2d\u4e26\u975e\u4f7f\u7528\u4eba\u5de5\u6a19\u8a18\u7684\u7b54\u6848\u9032\u884c\u9a57\u8b49\uff0c\u800c\u662f\u4f7f\u7528 \u81ea\u52d5\u6a19\u8a18\u7684\u7b54\u6848\u3002\u70ba\u4e86\u4e86\u89e3\u4f7f\u7528\u67d0\u4e00\u8a9e\u6599\u6240\u8a13\u7df4\u51fa\u7684\u6a21\u578b\u662f\u5426\u80fd\u61c9\u7528\u5728\u53e6\u4e00\u4e0d\u540c\u8a9e\u6599\u7684 \u6e2c\u8a66\u8cc7\u6599\uff0c\u6211\u5011\u5c0d\u500b\u5225\u7db2\u9801\u8207 Search Snippets \u9032\u884c\u4e86\u4ea4\u53c9\u6e2c\u8a66\uff0c\u6211\u5011\u4ee5\u5b8c\u6574\u7db2\u9801\u70ba\u8a13\u7df4 \u8cc7\u6599\u6240\u8a13\u7df4\u51fa\u7684\u6a21\u578b\u5c0d Snippet \u7684\u6e2c\u8a66\u8cc7\u6599\u9032\u884c\u6e2c\u8a66\uff0c\u540c\u6642\u4e5f\u4ee5 Snippet \u4e2d\u5169\u7a2e\u8a13\u7df4\u8cc7\u6599 \u6240\u8a13\u7df4\u51fa\u7684\u6a21\u578b\u5c0d 410 \u500b\u7db2\u9801\u9032\u884c\u6e2c\u8a66\u3002 \u5be6\u9a57\u7d50\u679c\u5982\u8868 4 \u6240\u793a\uff0c\u5716\u4e2d\u986f\u793a\u4e0d\u8ad6\u662f\u4f55\u7a2e\u6e2c\u8a66\u8cc7\u6599\u985e\u578b\uff0c\u7531 SnippetFullLabeling \u6240\u8a13\u7df4\u51fa\u7684\u6a21\u578b\u90fd\u5177\u6709\u6bd4\u8f03\u597d\u7684\u8fa8\u8b58\u6548\u679c\uff0c\u751a\u81f3\u6bd4\u500b\u5225\u5b8c\u6574\u7db2\u9801\u6240\u8a13\u7df4\u51fa\u7684\u6a21\u578b\u7528\u5728\u6e2c 190000 NER, 0.791 Match, 0.632 0 20000 40000 60000 80000 100000 120000 140000 160000 180000 200000 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 \u5728\u5be6\u9a57\u904e\u7a0b\u4e2d\uff0c\u6211\u5011\u767c\u73fe\u555f\u767c\u5f0f\u7684\u914d\u5c0d\u898f\u5247\u96d6\u7136\u53ef\u4ee5\u63d0\u6607 Detail Pages \u7684\u914d\u5c0d\u6b63\u78ba \u7387\uff0c\u4f46\u662f\u5176\u9918\u985e\u578b\u4f9d\u7136\u5f88\u4ef0\u8cf4\u547d\u540d\u5be6\u9ad4\u7684\u8fa8\u8a8d\u7d50\u679c\u3002\u82e5\u8981\u66f4\u9032\u4e00\u6b65\u63d0\u6607\u5546\u5bb6\u540d\u7a31\u7684\u8fa8\u8b58 \u7d50\u679c\uff0c\u6211\u5011\u89ba\u5f97\u53ef\u4ee5\u671d\u5169\u500b\u65b9\u5411\u9032\u884c\uff0c\u7b2c\u4e00\uff0c\u5fc5\u9808\u5c07\u5916\u90e8\u7279\u5fb5\u52a0\u5165\u7279\u5fb5\u77e9\u9663\u4e2d\uff0c\u56e0\u70ba\u5916 \u90e8\u7279\u5fb5\u96d6\u7136\u4e0d\u80fd\u660e\u78ba\u6307\u51fa\u5546\u5bb6\u540d\u7a31\uff0c\u4f46\u662f\u4f9d\u7136\u662f\u9032\u884c\u63a8\u6e2c\u7684\u91cd\u8981\u63d0\u793a\uff0c\u5728\u672a\u4f86\u6211\u5011\u5e0c\u671b \u80fd\u628a\u5916\u90e8\u7279\u5fb5\u548c\u8a5e\u983b\u52a0\u5165 CRF\uff0c\u63d0\u6607\u5546\u5bb6\u540d\u7a31\u7684\u8fa8\u8b58\u6548\u679c\u3002\u7b2c\u4e09\u662f\u5229\u7528\u5206\u6563\u5f0f\u7cfb\u7d71\u7684\u901f \u5ea6\uff0c\u5b8c\u6574\u6a19\u8a18\u5df2\u77e5(\u5927\u91cf)\u5df2\u77e5\u7684\u5546\u5bb6\u540d\u7a31\uff0c\u89e3\u6c7a\u81ea\u52d5\u6a19\u8a18\u7522\u751f\u7684\u8a13\u7df4\u8cc7\u6599\u54c1\u8cea\u4e0d\u4f73\u7684 \u554f\u984c\u3002 \u53c3\u8003\u6587\u737b F1 (Auto Labeling, Score Match) \u8a66\u540c\u985e\u8cc7\u6599\u9084\u8981\u9ad8\uff0c\u53ef\u898b\u5728\u81ea\u52d5\u6a19\u8a18\u4e2d\uff0c\u53ea\u4f7f\u7528\u90e8\u4efd\u5df2\u77e5\u7684\u5546\u5bb6\u540d\u7a31\u6240\u7522\u751f\u7684\u8a13\u7df4\u8cc7\u6599\uff0c TrainSet1 TrainSet2 TrainSet3 TrainSet4 TrainSet5 Ahlers, D. (2013). Business entity retrieval and data provision for yellow pages by local \u4e26\u4e0d\u662f\u4e00\u500b\u597d\u7684\u65b9\u5f0f\uff0c\u6703\u5927\u5e45\u5ea6\u53d7\u5230\u96dc\u8a0a\u8207\u6a23\u672c\u6578\u9650\u5236\u7684\u5f71\u97ff\u3002 search. Integrating IR technologies for professional search, ECIR, 2013. \u5716 4 \u662f\u8a13\u7df4\u8cc7\u6599\u6578\u91cf\u5c0d Precision\u3001Recall\u3001F1 \u5f71\u97ff\u7684\u8da8\u52e2\u5716\uff0c\u5716\u4e2d\u986f\u793a\u7576\u8a13\u7df4\u8cc7\u6599 \u7d9c\u5408\u4ee5\u4e0a\u5be6\u9a57\u7d50\u679c\u4f86\u770b\uff0c\u6211\u5011\u8a8d\u70ba\u5f71\u97ff\u8fa8\u8b58\u6548\u80fd\u7684\u4e3b\u8981\u7684\u539f\u56e0\u6709\u4e09\u500b\uff1a\u7b2c\u4e00\u662f\u56e0\u70ba \u5716 6. SnippetFullLabeling \u4e0d\u540c\u8a13\u7df4\u8cc7\u6599\u6578\u91cf\u7684\u6a21\u578b\u4e2d\uff0cNER \u5c0d Match \u7684\u5f71\u97ff Ahlers, D. (2013). Lo major de dos idiomas -cross-lingual linkage of geotagged \u6578\u91cf\u9054\u5230 30,000 \u6a23\u672c\u5e8f\u5217\u6642\uff0c\u8fa8\u8b58\u6548\u679c\u4f9d\u7136\u53ea\u6709 0.328\uff0c\u96d6\u7136 Recall \u7372\u5f97\u63d0\u6607\uff0c\u4f46\u662f Precision \u4e5f\u8f03\u5927\u5e45\u7684\u4e0b\u964d\u3002\u4e3b\u8981\u7684\u539f\u56e0\u53ef\u80fd\u5728\u65bc\u6211\u5011\u4f7f\u7528\u81ea\u52d5\u6a19\u8a18\u7522\u751f\u8a13\u7df4\u8cc7\u6599\u6642\uff0c\u4e26 \u5546\u5bb6\u7d44\u7e54\u540d\u7a31\u5c6c\u65bc\u8b8a\u7570\u6027\u8f03\u5927\u7684\u4e00\u7a2e\u547d\u540d\u5be6\u9ad4\uff0c\u5728\u8a13\u7df4\u968e\u6bb5\u4e2d\uff0c\u8cc7\u6599\u7684\u6e96\u5099\u80fd\u5426\u76e1\u53ef\u80fd \u7684\u6db5\u84cb\u5404\u985e\u5546\u5bb6\u7d44\u7e54\u540d\u7a31\u7684\u7279\u6027\u3002\u7b2c\u4e8c\uff0c\u7db2\u9801\u5c6c\u65bc\u4e00\u7a2e\u7d50\u69cb\u8907\u96dc\u7684\u8cc7\u6599\u4f86\u6e90\uff0c\u4ee5\u6b64\u7a2e\u8cc7 5. \u7d50\u8ad6 Wikipedia articles. Advances in Information Retrieval, 2013, 668-671.
", "type_str": "table", "num": null, "html": null }, "TABREF1": { "text": "", "content": "
AccuracyPrecisionRecallF-scoreStd. Dev.
0.8500.8500.8590.8550.040
", "type_str": "table", "num": null, "html": null }, "TABREF2": { "text": "", "content": "
TypeKeywordIGTypeKeywordIG
support \u7af6\u722d(Competition)0.1242 oppose \u53cd\u670d\u8cbf(Anti-CSSTA) 0.1013
support \u7e3d\u7d71(President)0.0862 oppose \u5b78\u904b(Movement)0.1013
support \u908a\u7de3\u5316(Marginalization)0.0628 oppose \u570b\u6c11\u9ee8(KMT)0.0996
support \u7834\u58de(Destroy)0.0603 oppose \u5be9\u8b70(Deliberation)0.0804
support \u843d\u5f8c(Fall behind)0.0444 oppose \u6c11\u4e3b(Democracy)0.0638
support \u8cbf\u6613\u5925\u4f34(Trading partners) 0.0412 oppose \u8df3\u91dd(Skipping)0.0628
support \u5229\u5927\u65bc\u5f0a(Good than harm)0.0402 oppose \u884c\u52d5(Action)0.0528
", "type_str": "table", "num": null, "html": null }, "TABREF3": { "text": "", "content": "
LabelDateEvent
AMar. 18Occupation of the Legislative Yuan
BMar. 23Occupation of the Executive Yuan
CMar. 28Rejection of the appeals by the Premier Jiang
DMar. 30Demonstration
EApr. 1March of the supporters
FApr. 6Declaration of the President of the Legislative Yuan
GApr. 7Announcement of the evacuation by the student leader Lin
", "type_str": "table", "num": null, "html": null }, "TABREF4": { "text": "\uff0c\u7d50\u5c3e\u6642\u7531\u5c08\u800c\u5ee3 (from specific to general) \u3002Swales (1990) \u66f4\u70ba\u7c21\u4ecb\u9019\u4e00\u500b\u5c0f\u7bc0\uff0c\u63d0\u51fa\u4e86\u6240\u8b02\u7684 CARS \u6a21\u5f0f(\u4ea6\u5373\u300c\u5275\u9020\u7814\u7a76\u7684\u7a7a\u9593\u300d\"Create a Research Space\") \u3002CARS \u6a21\u5f0f\u6b78\u7d0d\u4e86\u5178\u578b\u7684\u5b78\u8853\u8ad6\u6587\u7c21\u4ecb\u4fee\u8fad\u7684\u52d5\u6a5f\u8207\u6a21\u5f0f\u3002CARS \u6a21 \u5f0f\u63d0\u51fa\u4e4b\u5f8c\uff0c\u5ee3\u6cdb\u5730\u70ba\u5b78\u8005\u63a1\u7528\u4f5c\u70ba\u5206\u6790\u8ad6\u6587\u300c\u7c21\u4ecb\u300d\u7bc0\u7684\u5beb\u4f5c\u4fee\u8fad\u7b56\u7565", "content": "
\u5b78\u8853\u8ad6\u6587\u7c21\u4ecb\u7684\u81ea\u52d5\u6587\u6b65\u5206\u6790\u8207\u5beb\u4f5c\u63d0\u793a31 \u9ec3\u51a0\u8aa0 \u7b49
CARS \u6587\u6b65 WriteAhead \u6587\u6b65 \u8cc7\u8a0a\u5167\u5bb9 \u5b50\u6587\u6b65\u8207\u8cc7\u8a0a\u5167\u5bb9 WriteAhead \u80fd\u5920\u63d0\u4f9b\u8207\u6392\u5217\u9019\u4e9b\u63d0\u793a\uff0c\u662f\u56e0\u70ba WriteAhead \u900f\u904e\u5927\u91cf\u7684\u8ad6\u6587\u539f\u59cb\u8cc7 \u5c0d\u61c9\u4e4b CARS \u6587\u6b65
\u6587\u6b65 I \u754c\u5b9a\u7bc4\u570d \u80cc\u666f(BKG) \u6599\u4ee5\u53ca\u5c11\u91cf\u7684\u4eba\u5de5\u6a19\u793a\uff0c\u5b78\u7fd2\u5982\u4f55\u8fa8\u8b58 OWN \u6587\u6b65\u7684\u53e5\u5b50\uff0c\u4e26\u9032\u800c\u7d71\u8a08\u9019\u4e9b\u53e5\u5b50\u5167\u7684\u5e38 1. \u8072\u660e\u7814\u7a76\u9818\u57df\u7684\u91cd\u8981\u6027\uff0c\u53ca/\u6216 2. \u8072\u660e\u7814\u7a76\u8ab2\u984c\u7684\u5ee3\u6cdb\u6027\u8207\u666e\u53ca\u6027\uff0c\u53ca/\u6216 \u9818\u57df\uff1a\u91cd\u8981\u6027\u3001\u8853\u8a9e\u5b9a\u7fa9\u3001\u7f3a\u53e3 \u5f15\u7528\u8207\u8a55\u8ad6\u524d\u4eba\u7814\u7a76 \u6587\u6b65 I-1,2,3, \u6587\u6b65 II-1B \u6587\u6b65 I-3 \u898b\u7247\u8a9e\u53ca\u5176\u983b\u7387\u3002\u6211\u5011\u5c07\u5728\u7b2c\u4e09\u7bc0\u8a73\u8ff0 WriteAhead \u6240\u904b\u7528\u7684\u6587\u6b65\u5206\u985e\u5668\u7684\u8a13\u7df4\u904e\u7a0b\u3002
3. \u56de\u9867\u8207\u8a55\u8ad6\u524d\u4eba\u7814\u7a76 1A. \u63d0\u51fa\u8207\u524d\u4eba\u4e0d\u540c\u7684\u8072\u660e\uff0c\u6216 1B. \u6307\u51fa\u524d\u4eba\u7814\u7a76\u7684\u7f3a\u53e3(gap) \uff0c\u6216 \u672c\u8ad6\u6587(OWN) \u76ee\u7684\uff1a\u8f38\u5165\u3001\u8f38\u51fa\u3001\u689d\u4ef6 \u6587\u6b65 II \u5efa\u7acb\u5229\u57fa \u65b9\u6cd5\uff1a\u7814\u7a76\u8def\u7dda\u3001\u5178\u7bc4\u3001\u4f9d\u64da\u3001\u6b65\u9a5f \u7d50\u679c\uff1a\u5be6\u4f5c\u3001\u5be6\u9a57\u3001\u8a55\u4f30\u3001\u7d50\u679c\u3001\u767c\u73fe \u672c\u8ad6\u6587\u63a5\u4e0b\u4f86\u7684\u90e8\u5206\uff0c\u5b89\u6392\u5982\u4e0b\u3002\u6211\u5011\u5728\u4e0b\u4e00\u7bc0\u56de\u9867\u76f8\u95dc\u7684\u7814\u7a76\u3002\u63a5\u8457\uff0c\u6211\u5011\u63cf\u8ff0 \u6587\u6b65 III-1A, \u6587\u6b65 II-1C \u5982\u4f55\u5b78\u7fd2\u81ea\u52d5\u5c07\u8ad6\u6587\u7c21\u4ecb\u53e5\u5b50\u6a19\u8a3b\u6587\u6b65(\u7b2c\u4e09\u7bc0) \u3002\u6211\u5011\u7e7c\u800c\u63cf\u8ff0\u5982\u4f55\u5c07\u6240\u63d0\u51fa\u7684\u65b9\u6cd5\uff0c \u6587\u6b65 III-1B, \u6587\u6b65 II-1D \u5be6\u969b\u88fd\u4f5c\u6210\u4e00\u500b\u8003\u616e\u6587\u6b65\u985e\u5225\u9032\u884c\u5beb\u4f5c\u63d0\u793a\u7684\u96db\u5f62\u7cfb\u7d71\uff0c\u4ee5\u53ca\u76f8\u95dc\u7684\u5be6\u9a57\u8a2d\u5b9a\u3001\u8a55\u4f30\u6307 \u6587\u6b65 III-2 \u6a19\u3001\u4ee5\u53ca\u5be6\u9a57\u7d50\u679c(\u7b2c\u56db\u7bc0)\u3002\u6700\u5f8c\uff0c\u6211\u5011\u6307\u51fa\u672a\u4f86\u7814\u7a76\u65b9\u5411\uff0c\u4e26\u4f5c\u7d50\u8ad6(\u7b2c\u4e94\u7bc0)\u3002
\u8a0e\u8ad6(DIS) 2. \u76f8\u95dc\u6587\u737b1C. \u63d0\u51fa\u672c\u8ad6\u6587\u7684\u7814\u7a76\u8b70\u984c(research question) \uff0c\u6216 1D. \u8aaa\u660e\u672c\u7814\u7a76\u6240\u6839\u64da\u7684\u5178\u7bc4\u8207\u50b3\u7d71 \u6bd4\u8f03\u672c\u8ad6\u6587\u8207\u524d\u4eba\u7814\u7a76\u7684\u76f8\u540c\u4e4b\u8655 \u5c0d\u7167\u672c\u8ad6\u6587\u8207\u524d\u4eba\u7814\u7a76\u7684\u76f8\u7570\u4e4b\u8655 \u6587\u6b65 II-1A
\u6587\u6b65 III \u5b78\u8853\u82f1\u6587\u7814\u7a76\u8207\u6559\u5b78(English for Academic Purpose)\u70ba\u76f8\u7576\u91cd\u8981\u7684\u7814\u7a76\u9818\u57df\u3002\u8fd1\u5e74\u4f86\uff0c 1A. \u6982\u8ff0\u672c\u8ad6\u6587\u7684\u76ee\u7684\uff0c\u6216 \u672a\u4f86\u7814\u7a76\u65b9\u5411
Keywords: Academic English Writing, Computer-assisted Language Learning, \u4f54\u64da\u5229\u57fa 1B. \u6982\u8ff0\u672c\u8ad6\u6587\u7684\u65b9\u6cd5 \u6587\u672c\u7d44\u7e54(TEX) \u63d0\u4f9b\u5168\u6587\u7684\u7bc0\u5927\u7db1(\u76ee\u6b21\u8868) \u6587\u6b65 III-3 \u5b78\u8005\u5c0d\u65bc\u7814\u7a76\u8a08\u5283\u66f8\uff0c\u4ee5\u53ca\u5b78\u8853\u6703\u8b70\u8207\u671f\u520a\u8ad6\u6587\uff0c\u90fd\u6709\u6df1\u5165\u7684\u7814\u7a76(Connor & Mauranen,
Rhetoric, Context Analysis 2. \u5ba3\u5e03\u672c\u8ad6\u6587\u7684\u4e3b\u8981\u7d50\u679c\u8207\u767c\u73fe \u63d0\u4f9b\u7bc0\u5167\u7d30\u5206\u5b50\u7bc0\u7684\u5927\u7db1 1999; Swales & Feak, 2004) \u3002\u9019\u4e9b\u7814\u7a76\u901a\u5e38\u91dd\u5c0d\u8ad6\u6587\u9010\u53e5\u9010\u6bb5\u9032\u884c\u4eba\u70ba\u5206\u6790\uff0c\u7d93\u904e\u6b78\u7d0d
3. \u6307\u51fa\u672c\u8ad6\u6587\u7684\u7d50\u69cb \u6307\u793a\u5716\u8868(\u7de8\u865f) \u5f8c\uff0c\u63d0\u51fa\u4e00\u5957\u8ad6\u6587\u4fee\u8fad\u7684\u5206\u6790\u67b6\u69cb\u3002\u5728\u672c\u7814\u7a76\u4e2d\uff0c\u6211\u5011\u5247\u91dd\u5c0d\u5b78\u8853\u8ad6\u6587\u7684\u300c\u7c21\u4ecb\u300d\u9019\u4e00 1. \u7c21\u4ecb \u8fd1\u5e74\u4f86\uff0c\u82f1\u6587\u9010\u6f38\u8b8a\u6210\u5168\u4e16\u754c\u5b78\u8853\u7814\u7a76\u6700\u4e3b\u8981\u7684\u6e9d\u901a\u7684\u5a92\u4ecb\u3002\u800c\u5b78\u8853\u82f1\u6587\u5beb\u4f5c\uff0c\u4e5f\u6210\u70ba \u975e\u5e38\u91cd\u8981\u7684\u7814\u7a76\u8207\u6559\u5b78\u7684\u9818\u57df\u3002\u5b78\u8005\u4e5f\u5f88\u91cd\u8996\uff0c\u5982\u4f55\u900f\u904e\u96fb\u8166\u7684\u8f14\u52a9\uff0c\u5e6b\u52a9\u4e00\u822c\u6027\u7684\u8a9e \u500b\u90e8\u5206\uff0c\u63d0\u51fa\u4e00\u5957\u81ea\u52d5\u5316\u7684\u7d50\u69cb\u5206\u6790\u65b9\u6cd5\uff0c\u4e26\u958b\u767c\u4e00\u5957\u80fd\u5920\u8b93\u5b78\u751f\u4e00\u9762\u5beb\u4f5c\uff0c\u4e00\u9762\u7372\u5f97 \u56de\u9867\u4e4b\u524d\u8cc7\u8a0a\u3001\u9810\u544a\u4e4b\u5f8c\u8cc7\u8a0a \u5beb\u4f5c\u63d0\u793a\u7684\u96fb\u8166\u8f14\u52a9\u5beb\u4f5c\u7cfb\u7d71\u3002\u6211\u5011\u4e5f\u8a0e\u8ad6\u5982\u4f55\u5728\u53e5\u5b50\u4e2d\uff0c\u64f7\u53d6\u80fd\u53cd\u61c9\u4fee\u8fad\u7d50\u69cb\u7684\u7279\u5fb5\uff0c \u5716 2. WriteAhead \u63a1\u7528\u6587\u6b65\u8207 CARS \u6a21\u5f0f\u6587\u6b65\u4e4b\u5c0d\u7167 \u4ee5\u6709\u52a9\u65bc\u7522\u751f\u8a13\u7df4\u8cc7\u6599\uff0c\u5c07\u53e5\u5b50\u6b78\u985e\u3002
\u8a00\u5b78\u7fd2\uff0c\u751a\u6216\u7279\u5b9a\u6027\u7684\u5b78\u8853\u5beb\u4f5c\u3002\u5b78\u8853\u5beb\u4f5c\u5305\u542b\u8a31\u591a\u7684\u6587\u7ae0\u985e\u578b\uff0c\u5305\u62ec\u5b78\u8853\u8ad6\u6587\u3001\u8a08\u756b \u8a31\u591a\u5b78\u8005\u90fd\u6307\u51fa\uff0c\u5728\u8868\u9762\u4e0a\u4ee5\u53ca\u5c0f\u7bc0\u5206\u6bb5\u4e0a\uff0c\u7814\u7a76\u8ad6\u6587\u5927\u81f4\u4e0a\u6709\u5171\u901a\u7684\u7c21\u55ae\u7d50\u69cb\u2500
\u7533\u8acb\u66f8\u3001\u56de\u9867\u8207\u8a55\u8ad6\u6587\u7ae0\u7b49(Swales, 1990)\u3002\u5176\u4e2d\uff0c\u7814\u7a76\u8ad6\u6587\u5360\u6709\u6700\u91cd\u8981\u7684\u89d2\u8272\u3002 \u5728\u5b78\u8853\u8ad6\u6587\u4e2d\uff0c\u300c\u7c21\u4ecb\u300d\u662f\u7d55\u5927\u90e8\u5206\u8ad6\u6587\u90fd\u6709\u7684\u7b2c\u4e00\u500b\u5c0f\u7bc0\u3002\u73fe\u4eca\uff0c\u5e7e\u4e4e\u6c92\u6709\u5b78\u8853 \u8ad6\u6587\uff0c\u6c92\u6709\u300c\u6458\u8981\u300d\u8207\u300c\u7c21\u4ecb\u300d\uff0c\u800c\u76f4\u63a5\u8a73\u7d30\u5730\u63cf\u8ff0\u7814\u7a76\u7684\u76ee\u7684\u3001\u65b9\u6cd5\u3001\u7d50\u679c\u3002\u800c\u4e14\uff0c \u80fd\u70ba\u6574\u7bc7\u8ad6\u6587\u5b9a\u8abf\uff0c\u6293\u4f4f\u8b80\u8005\u7684\u8208\u8da3\uff0c\u63d0\u4f9b\u8ad6\u6587\u7684\u627c\u8981\u8cc7\u8a0a\u3002\u63db\u8a00\u4e4b\uff0c\u300c\u7c21\u4ecb\u300d \u80a9\u8ca0\u91cd \u904e\u7a0b\uff0c\u6709\u6548\u5730\u5354\u52a9\u5b78\u751f\u3002 \u5c0d\u5beb\u8005\u548c\u8b80\u8005\u800c\u8a00\uff0c\u300c\u7c21\u4ecb\u300d\u5728\u5b78\u8853\u8ad6\u6587\u4e2d\u90fd\u626e\u6f14\u975e\u5e38\u91cd\u8981\u7684\u89d2\u8272\u3002\u4e00\u7bc7\u597d\u7684\u7c21\u4ecb\uff0c\u8981 \u2500IMRD \u7d50\u69cb\uff0c\u5373\u7c21\u4ecb (introduction) \u3001\u65b9\u6cd5 (method) \u3001\u7d50\u679c (results) \u3001\u8a0e\u8ad6 (discussion) \u3002 \u6587\u4e26\u81ea\u52d5\u7522\u751f\u6279\u6539\u7684\u5efa\u8b70\u8207\u8a55\u5206\u3002\u4f46\u662f\u5f88\u5c11\u6709\u7cfb\u7d71\u80fd\u5920\u5728\u5b78\u751f\u5beb\u4f5c\u4e2d\uff0c\u4f9d\u7167\u6587\u6b65\u7684\u63a8\u9032\uff0c \u4e5f\u6709\u5b78\u8005\u9032\u4e00\u6b65\u95e1\u8ff0 IMRD \u7684\u4fee\u8fad\u7d50\u69cb\uff0c\u5c31\u50cf\u4e0a\u4e0b\u5bec\u5927\uff0c\u4e2d\u9593\u72f9\u7a84\u7684\u6c99\u6f0f\uff1a\u958b\u59cb\u6642\u5148 \u9069\u6642\u5730\u63d0\u4f9b\u5beb\u4f5c\u63d0\u793a\u8207\u8f14\u52a9\u3002\u76f4\u89ba\u4e0a\uff0c\u5982\u679c\u6211\u5011\u80fd\u5c07\u5927\u91cf\u7684\u8ad6\u6587\u7c21\u4ecb\u52a0\u4ee5\u8655\u7406\uff0c\u81ea\u52d5\u5316 \u5206\u6790\u5176\u4e2d\u6bcf\u53e5\u7684\u6587\u6b65\uff0c\u7e7c\u800c\u5206\u6790\u7279\u5b9a\u6587\u6b65\u53e5\u5b50\u7684\u5e38\u898b\u7247\u8a9e\u6216\u53e5\u578b\uff0c\u6211\u5011\u5c07\u53ef\u4ee5\u5728\u5beb\u4f5c\u7684 \u5ee3\u5f8c\u5c08 (from general to specific)
\u5927\u8cac\u4efb\u2500\u2500\u5438\u5f15\u8b80\u8005\u6ce8\u610f\uff0c\u8b80\u5b8c\u5168\u6587\u3002 \u7136\u800c\uff0c\u904e\u53bb\u6240\u63d0\u51fa\u7684\u81ea\u52d5\u6587\u6b65\u5206\u6790\u65b9\u6cd5\uff0c\u90fd\u9700\u8cbb\u6642\u8cbb\u5de5\u6a19\u8a3b\u5927\u91cf\u8ad6\u6587\u3002\u6709\u9451\u65bc\u6b64\uff0c
\u6211\u5011\u63d0\u51fa\u65b0\u65b9\u6cd5\uff0c\u4ee5\u964d\u4f4e\u4eba\u5de5\u6a19\u8a3b\u7684\u5de5\u4f5c\u91cf\uff0c\u4e14\u6a19\u6ce8\u4e4b\u8cc7\u6599\u5c07\u904b\u7528\u65bc\u8a13\u7df4\u7d71\u8a08\u5f0f\u5206\u985e\u5668\uff0c \u56e0\u6b64\uff0c\u6709\u4e00\u4e9b\u7814\u7a76\u958b\u59cb\u5206\u6790\u8ad6\u6587\u7c21\u4ecb\u5982\u4f55\u9054\u6210\u5176\u6e9d\u901a\u7684\u4efb\u52d9\u3002Graetz (1985) \u767c\u73fe\u8ad6 \u4f86\u9810\u6e2c\u8ad6\u6587\u7c21\u4ecb\u4e2d\u53e5\u5b50\u7684\u6587\u6b65\uff0c\u4e26\u85c9\u4ee5\u958b\u767c\u4e00\u500b\u7dda\u4e0a\u8f14\u52a9\u5beb\u4f5c\u7cfb\u7d71 WriteAhead\u3002\u5728 \u6587\u7c21\u4ecb\u4f3c\u4e4e\u6709\u5171\u540c\u7684 \u300c\u554f\u984c\u2500\u89e3\u6cd5\u300d \u4fee\u8fad\u7d50\u69cb\uff0c\u4f9d\u5e8f\u5305\u62ec\u554f\u984c (problem) \u3001\u65b9\u6cd5 (solution) \u3001 WriteAhead \u7684\u958b\u767c\u904e\u7a0b\uff0c\u6211\u5011\u63a1\u7528\u4e86\u6bd4 CARS \u66f4\u7c21\u55ae\u7684\u6587\u6b65\u5206\u985e\uff0c\u5982\u5716 2 \u6240\u793a\u3002\u7528\u4e86 \u8a55\u4f30(evaluation)\u3001\u7d50\u8ad6(conclusion)\u7b49\u90e8\u5206\u3002 \u6b64\u4e00\u5206\u985e\u65b9\u5f0f\uff0c\u9664\u7cfb\u7d71\u8f03\u6613\u65bc\u81ea\u52d5\u5206\u985e\u6587\u6b65\u5916\uff0c\u4f7f\u7528\u8005\u4ea6\u6bd4\u8f03\u5bb9\u6613\u638c\u63e1\u4e26\u4f7f\u7528\u65bc\u5beb\u4f5c\u904e
\u7a0b\u3002Swales (1990) \u5206\u6790\u5927\u91cf\u7684\u8ad6\u6587\u7c21\u4ecb\uff0c\u6b78\u7d0d\u51fa\u4e00\u5957\u4fee\u8fad\u7684\u52d5\u6a5f\u8207\u6a21\u5f0f\uff1a\u300c\u5275\u9020\u7814\u7a76\u7a7a
\u9593\u300d(Create A Research Space, CARS)\u3002Swales \u8a8d\u70ba\u8ad6\u6587\u722d\u53d6\u7814\u7a76\u5f97\u5230\u8b80\u8005\u7684\u8a8d\u540c\uff0c \u6211\u5011\u671f\u671b\u6b64\u4e00\u81ea\u52d5\u6587\u6b65\u5206\u6790\u5de5\u5177\uff0c\u4ee5\u53ca WriteAhead \u7cfb\u7d71\uff0c\u6709\u52a9\u65bc\u63d0\u5347\u82f1\u6587\u975e\u6bcd\u8a9e \u6709\u5982\u74b0\u5883\u4e2d\u751f\u7269\u722d\u53d6\u751f\u5b58\u7a7a\u9593\u3002\u70ba\u6b64\uff0c\u5927\u90e8\u5206\u4f5c\u8005\u4f9d\u5faa\u4e09\u500b\u4fee\u8fad\u7684\u6b65\u9a5f\u2500\u2500\u4e5f\u5c31\u662f\u6587\u6b65 \u8005(non-native speakers, NNS)\u5beb\u4f5c\u5b78\u8853\u8ad6\u6587\u7684\u80fd\u529b\u3002\u5728\u672c\u8ad6\u6587\u4e2d\uff0c\u6211\u5011\u63d0\u51fa\u4e86\u4e00\u5957\u76e3\u7763 (moves)\u2500\u2500\u4f86\u8aaa\u670d\u8b80\u8005\u3002\u5982\u5716 1 \u6240\u793a\uff0c\u9019\u4e09\u500b\u6587\u6b65\u5305\u62ec\u4e86\u300c\u754c\u5b9a\u7814\u7a76\u7bc4\u570d\u300d\u3001\u300c\u5efa \u5f0f\u6a5f\u5668\u5b78\u7fd2\u7684\u65b9\u6cd5\uff0c\u80fd\u5920\u81ea\u52d5\u5730\u5b78\u7fd2\u5982\u4f55\u5c07\u8a9e\u6599\u5eab\u5167\u7684\u7c21\u4ecb\u53e5\u5b50\uff0c\u5927\u7565\u5730\u5206\u985e\u70ba\u5e7e\u500b\u6587 \u5716 3. WriteAhead \u7cfb\u7d71\u64cd\u4f5c\u7bc4\u4f8b \u7acb\u5229\u57fa\u300d\u3001\u300c\u4f54\u64da\u5229\u57fa\u300d\u3002\u5728\u6bcf\u4e00\u500b\u6587\u6b65\u4e0b\uff0c\u53c8\u9700\u8981\u63cf\u8ff0\u82e5\u5e72\u5fc5\u8981\u6216\u9078\u9805\u7684\u5167\u5bb9\u3002\u53e6\u5916\uff0c \u7f8e\u570b\u570b\u5bb6\u91ab\u5b78\u5716\u66f8\u9928\uff0c\u4e5f\u4e3b\u5f35\u91ab\u5b78\u8ad6\u6587\u4f5c\u8005\uff0c\u61c9\u63d0\u4f9b\u5206\u6bb5\u6709\u6a19\u984c(labeled sections)\u7684\u7d50 \u69cb\u5316\u6458\u8981(structured abstract) 1 \u3002 \u6b65\u3002\u6709\u4e86\u5206\u985e\u7684\u53e5\u5b50\u4e4b\u5f8c\uff0c\u6211\u5011\u5c31\u53ef\u4ee5\u7d71\u8a08\u5404\u6587\u6b65\u7684 N \u9023\u8a5e (ngrams) \u8a5e\u983b\u3002\u5728 WriteAhead \u5716 3 \u986f\u793a WriteAhead \u7cfb\u7d71\u7684\u64cd\u4f5c\u5be6\u4f8b\u3002\u5728\u5716\u4e2d\uff0c\u4f7f\u7528\u8005\u5df2\u7d93\u4ecb\u7d39\u4e86\u7814\u7a76\u80cc\u666f \u7cfb\u7d71\uff0c\u5373\u53ef\u53c3\u8003\u4f7f\u7528\u8005\u9078\u64c7\u7684\u6587\u6b65\uff0c\u4ee5\u53ca\u6e38\u6a19\u4e4b\u524d\u7684\u5167\u5bb9\uff0c\u63d0\u793a\u55ae\u5b57\u4ee5\u53ca\u63a5\u7e8c\u7247\u8a9e\u3002 (BKG \u6587\u6b65)\uff0c\u63a5\u8457\u4f7f\u7528\u8005\u9078\u64c7\u4e86\u300c\u672c\u8ad6\u6587\u6587\u6b65\u300d (OWN)\uff0c\u7e7c\u800c\u8f38\u5165\"In this paper\" \u7b49
\u5b57\u3002\u6839\u64da\u9019\u4e9b\u8cc7\u8a0a\uff0cWriteAhead \u986f\u793a\u4e86\u9069\u5408\u6b64\u4e00\u8108\u7d61\u7684\u63d0\u793a\u5982\u4e0b\uff0c\u4f5c\u70ba\u7e7c\u7e8c\u5beb\u4f5c\u7684\u53c3\u8003\uff1a
, we present, we describe, we explore
, we propose, we will, we show
", "type_str": "table", "num": null, "html": null }, "TABREF5": { "text": "\u3002\u8207\u4e0a\u8ff0\u7814\u7a76\u4e0d\u540c\uff0c\u6211\u5011\u63a1\u7528\u4eba \u5de5\u7763\u5c0e\u8207\u6a5f\u5668\u5b78\u7fd2\u7684\u65b9\u5f0f\uff0c\u81ea\u52d5\u5316\u5206\u985e\u8207\u6a19\u8a3b\u300c\u7c21\u4ecb\u300d\u7bc0\u4e2d\u53e5\u5b50\u7684\u6587\u6b65\u3002 \u5728\u81ea\u7136\u8a9e\u8a00\u8655\u7406\u7684\u7814\u7a76\u9818\u57df\uff0cAnthony & Lashkia (2003) \u6536\u96c6\u4e86\u8fd1 700 \u7bc7\u8ad6\u6587\u6458\u8981\uff0c \u4e26\u904b\u7528\u4e86 CARS \u6a21\u5f0f\uff0c\u4eba\u5de5\u6a19\u793a\u6458\u8981\u4e2d\u6bcf\u53e5\u7684\u6587\u6b65\u3002\u4e4b\u5f8c\uff0c\u518d\u900f\u904e\u6a5f\u5668\u5b78\u7fd2\u65b9\u6cd5\uff0c\u767c \u5c55\u51fa\u81ea\u52d5\u6587\u6b65\u6a19\u793a\u7cfb\u7d71 MOVER\u3002Anthony \u904b\u7528 MOVER \u65bc\u5b78\u8853\u5beb\u4f5c\u6559\u5b78\uff0c\u767c\u73fe\u53ef\u4ee5\u5e6b \u52a9\u5b78\u751f\u95b1\u8b80\u3001\u5206\u6790\u3001\u5beb\u4f5c\u6458\u8981\uff0c\u8b93\u5b78\u751f\u6709\u4fe1\u5fc3\u5730\u5beb\u51fa\u6458\u8981\u7684\u8349\u7a3f\uff0c\u7a81\u7834\u6c92\u6709\u4f7f\u7528\u8f14\u52a9\u7cfb \u7d71\u6642\uff0c\u5bb9\u6613\u7336\u8c6b\u4e0d\u6c7a\uff0c\u4e45\u4e45\u96e3\u4ee5\u4e0b\u7b46\u7684\u969c\u7919\u3002\u7136\u800c\uff0cAnthony \u767c\u73fe CARS \u7684\u6587\u6b65\u5283\u5206\u592a \u7d30\uff0c\u9020\u6210 MOVER \u6a19\u793a\u6587\u6b65\u7684\u7cbe\u78ba\u5ea6\u4e0d\u9ad8\u3002\u4ed6\u5efa\u8b70\u5408\u4f75\u76f8\u95dc\u6613\u6df7\u6dc6\u6587\u6b65\u3002\u5982\u6b64\uff0c\u53ef\u4ee5\u5927 \u5e45\u5ea6\u63d0\u9ad8 MOVER \u6587\u6b65\u5206\u985e\u7684\u6b63\u78ba\u5ea6\uff0c\u4e5f\u4e0d\u81f3\u65bc\u904e\u65bc\u5f71\u97ff MOVER \u7684\u6548\u7528\u3002\u6211\u5011\u4e5f\u5c07 CARS \u7684 3 \u5927\u6587\u6b65\u5171 11 \u5c0f\u6587\u6b65\uff0c\u5408\u4f75\u70ba 4 \u500b\u6587\u6b65\uff0c\u4ee5\u63d0\u6607\u5206\u985e\u6b63\u78ba\u5ea6\uff0c\u540c\u6642\u4e5f\u6e1b\u4f4e \u4f7f\u7528\u8005\u7684\u8a8d\u77e5\u8ca0\u64d4\u3002 \u9ec3\u51a0\u8aa0 \u7b49 \u4e0d\u540c\u7684\u5b78\u8853\u9818\u57df\u7684\u793e\u7fa4\u6709\u4e0d\u540c\u6587\u5316\u8207\u6e9d\u901a\u7684\u6a21\u5f0f\u3002\u91ab\u5b78\u9818\u57df\u7684\u7de8\u8f2f\u8a8d\u70ba\u6458\u8981\u61c9\u5206\u6210 \u6709\u6a19\u984c\u7684\u5340\u6bb5\uff0c\u4ea6\u5373\u6240\u8b02\u7d50\u69cb\u5316\u6458\u8981(structural abstracts)\u3002\u7d50\u69cb\u5316\u6458\u8981\u53ef\u4ee5\u8b93\u4f5c\u8005\u5beb\u51fa \u7684\u6458\u8981\uff0c\u8cc7\u8a0a\u5b8c\u6574\u3001\u6d41\u66a2\u6613\u8b80(Harley, 2000)\u3002\u5176\u5be6\u9019\u4e9b\u6709\u6a19\u984c\u7684\u4e00\u5230\u4e09\u53e5\u7684\u5c0f\u6bb5\uff0c\u548c \u6587\u6b65\u7684\u89c0\u5ff5\u662f\u4e00\u81f4\u7684\u3002Shimbo et al. (2003) \u904b\u7528\u4e86 MEDLINE \u91ab\u5b78\u6587\u737b\u8cc7\u6599\u5eab\u4e2d\u6a19\u6ce8\u5340 \u6bb5\u6216\u6587\u6b65\u7684\u6458\u8981\uff0c\u958b\u767c\u4e00\u5957\u5206\u5340\u6aa2\u7d22\u7684\u6587\u4ef6\u8cc7\u8a0a\u6aa2\u7d22\u7cfb\u7d71\u3002\u8a72\u7cfb\u7d71\u904b\u7528\u652f\u6490\u5411\u91cf\u6a5f (Support Vector Machine, SVM) \uff0c\u5c07\u6458\u8981\u4e2d\u7684\u53e5\u5b50\u5283\u5206\u70ba\u300c\u76ee\u7684\u300d \u3001 \u300c\u65b9\u6cd5\u300d \u3001 \u300c\u7d50\u679c\u300d\u300d \u300c\u7d50\u8ad6\u300d\u56db\u7a2e\u6587\u6b65\u3002Yamamoto & Takagi (2005) \u4e5f\u958b\u767c\u51fa\u985e\u4f3c\u7684 SVM \u7cfb\u7d71\uff0c\u53ef\u5c07\u53e5\u5b50 \u5206\u70ba \u300c\u80cc\u666f\u300d \u5728\u52a0\u4e0a\u4ee5\u4e0a\u56db\u985e\u7684\u6587\u6b65\u3002Hirohata et al. (2008) \u5247\u662f\u5229\u7528 CRF \u7cfb\u5217\u5206\u985e\u5668\uff0c \u4f86\u6a19\u793a\u6574\u500b\u6458\u8981\u3002\u9019\u4e9b\u7cfb\u7d71\u901a\u5e38\u5229\u7528\u7247\u8a9e\u3001\u52d5\u8a5e\u6642\u614b\u3001\u53e5\u5b50\u4f4d\u7f6e\u3001\u524d\u5f8c\u53e5\u7279\u5fb5\uff0c\u505a\u70ba\u5206 \u985e\u4f9d\u64da\u3002", "content": "
\u5b78\u8853\u8ad6\u6587\u7c21\u4ecb\u7684\u81ea\u52d5\u6587\u6b65\u5206\u6790\u8207\u5beb\u4f5c\u63d0\u793a35
3. \u65b9\u6cd5
\u70ba\u4e86\u80fd\u5920\u91dd\u5c0d\u5b78\u751f\u5beb\u4f5c\u8ad6\u6587\u904e\u7a0b\u4e2d\uff0c\u6240\u60f3\u8868\u9054\u7684\u8cc7\u8a0a(\u6587\u6b65) \uff0c\u63d0\u4f9b\u9069\u7576\u7684\u5beb\u4f5c\u63d0\u793a\uff0c\u6211
\u5011\u9700\u8981\u5927\u91cf\u6a19\u793a\u6587\u6b65\u6a19\u7c64\u7684\u53e5\u5b50\u3002\u4eba\u5de5\u9010\u53e5\u76f4\u63a5\u6a19\u793a\u6587\u6b65\uff0c\u7121\u7591\u5730\u975e\u5e38\u8cbb\u6642\u8017\u5de5\uff0c\u7d55\u975e
\u6700\u597d\u7684\u4f5c\u6cd5\u3002\u6bd4\u8f03\u6709\u6f5b\u529b\u7701\u6642\u7701\u529b\u7684\u65b9\u6cd5\uff0c\u662f\u5148\u64f7\u53d6\u4e00\u4e9b\u8ad6\u6587\u5c11\u91cf\u5e38\u898b\u53e5\u578b(\u4f8b\u5982\uff0c
\"Recently, there have been ...\") \uff0c\u900f\u904e\u4eba\u5de5\u6aa2\u8996\u9019\u4e9b\u53e5\u578b\u3002\u6c7a\u5b9a\u53e5\u578b\u662f\u5426\u5927\u90fd\u8868\u9054\u7279\u5b9a\u7684
\u6587\u6b65(\u5982\uff0c\u80cc\u666f\u8207\u91cd\u8981\u6027) \u3002\u5982\u679c\u53e5\u578b\u6709\u8868\u9054\u7279\u5b9a\u6587\u6b65\u7684\u50be\u5411\uff0c\u5c31\u53ef\u4ee5\u4fdd\u7559\u53e5\u578b\uff0c\u4e26\u6a19\u8a3b
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\u6587\u6b65\u5206\u985e\u5668\u7684\u8a13\u7df4\u8cc7\u6599\u3002
3.1 \u554f\u984c\u9673\u8ff0
\u6211\u5011\u8a66\u5716\u6536\u96c6\u5927\u91cf\u5b78\u8853\u8ad6\u6587\uff0c\u4e26\u5c0d\u5176\u4e2d\u7c21\u4ecb\u90e8\u5206\u7684\u6bcf\u500b\u53e5\u5b50\u90fd\u6a19\u8a3b\u4fee\u8fad\u6587\u6b65\u3002\u4e4b\u5f8c\uff0c\u6211
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", "type_str": "table", "num": null, "html": null }, "TABREF7": { "text": "Lashkia, G. V. (2003). Mover: A machine learning tool to assist in the reading and writing of technical papers. IEEETrans. Prof. Commun., 46,[185][186][187][188][189][190][191][192][193]. Teaching EFL students to extract structural information from abstracts. In JanM. Ulijn and Anthony K. Pugh, editors, Reading for Professional Purposed: Methods and Materials in Teaching Languages, pages 123-135. Acco, Leuven, Belgium. Hirohata, K., Okazaki, N., Ananiadou, S., Ishizuka, M., & Biocentre, M. I. (2008). Identifying Sections in Scientific Abstracts using Conditional Random Fields.McKnight, L.,& Srinivasan, P. (2003). Categorization of sentence types in medical abstracts.InAMIA Annual Symposium Proceedings (Vol. 2003, p. 440). American Medical Informatics Association.Ruch, P., Boyer, C., Chichester, C., Tbahriti, I., Geissb\u00fchler, A., Fabry, P., ... & Veuthey, A. L.Wu, J. C.,Chang, Y. C., Liou, H. C., & Chang, J. S. (2006). Computational analysis of move structures in academic abstracts.Yamamoto, Y., & Takagi, T. (2005). A sentence classification system for multi-document summarization in the biomedical domain. In Proceedings of International Workshop on Biomedical Data Engineering,90-95.", "content": "
\u5b78\u8853\u8ad6\u6587\u7c21\u4ecb\u7684\u81ea\u52d5\u6587\u6b65\u5206\u6790\u8207\u5beb\u4f5c\u63d0\u793a 3.2.3 \u4eba\u5de5\u6a19\u8a18\u5e38\u898b\u53e5\u578b\u4e4b\u6587\u6b65 \u5728\u8a13\u7df4\u7684\u7b2c\u4e09\u6b65\u9a5f\uff0c\u6211\u5011\u6311\u9078\u4e00\u4e9b\u9ad8\u983b\u4e14\u6587\u6b65\u7279\u6027\u660e\u986f\u7684\u7247\u8a9e\u4e26\u624b\u52d5\u5730\u6a19\u8a18\u4e0a\u6587\u6b65\u3002\u5728 37 \u6b64\u968e\u6bb5\uff0c\u6211\u5011\u5c07\u6587\u6b65\u5206\u70ba\u80cc\u666f(BKG)\u3001\u672c\u8ad6\u6587(OWN)\u3001\u8a0e\u8ad6(DIS)\u3001\u6587\u672c(TEX) \u56db\u7a2e\u985e\u578b\u3002 BKG \u90e8\u5206\u63cf\u8ff0\u9818\u57df\u3001\u8ab2\u984c\u3001\u7f3a\u53e3\u3001\u6587\u737b\uff0cOWN \u90e8\u5206\u63cf\u8ff0\u672c\u8ad6\u6587\u4e4b\u65b9\u6cd5\u3001\u7d50 \u679c\uff0cDIS \u90e8\u5206\u8a0e\u8ad6\u672c\u8ad6\u6587\u8207\u524d\u4eba\u4e4b\u512a\u52a3\u7570\u540c\uff0cTEX \u90e8\u5206\u63cf\u8ff0\u5168\u6587\u6216\u7bc0\u7684\u76ee\u7684\u8207\u7d44\u7e54\u3002 \u8868 1 \u986f\u793a\u6a19\u4e86\u6587\u6b65\u7684\u7247\u8a9e\u7bc4\u4f8b\uff0c\u4ee5\u53ca\u6a19\u7c64\u7684\u7c21\u55ae\u5b9a\u7fa9\u3002\u6240\u4ee5\u9019\u500b\u968e\u6bb5\u7684\u6a19\u8a3b\u5c0d\u8c61\u662f\u8655\u7406\u904e \u5f8c\u7684\u7247\u8a9e\u3002\u4eba\u5de5\u6a19\u8a3b\u7684\u904e\u7a0b\u4e2d\uff0c\u5f88\u96e3\u63a7\u5236\u6a19\u8a3b\u7684\u54c1\u8cea\uff0c\u56e0\u6b64\u6a19\u8a3b\u8005\u4e4b\u9593\u7684\u4e00\u81f4\u6027\uff0c\u9700\u7d93 \u53cd\u8986\u7684\u6838\u5c0d\uff0c\u8abf\u89e3\u6709\u885d\u7a81\u7684\u6a19\u8a18 \u3002 \u8868 1. \u6709\u6587\u6b65\u6a19\u8a18\u4e4b\u53e5\u578b\u7bc4\u4f8b \u6587\u6b65 \u53e5\u578b \u89e3\u91cb TEX in section , we review work \u6587\u672c\uff1a\u63cf\u8ff0\u5168\u6587\u6216\u7bc0\u7684\u76ee\u7684\u8207\u7d44\u7e54 BKG research support in part by NE \u80cc\u666f\uff1a\u63cf\u8ff0\u9818\u57df\u3001\u8ab2\u984c\u3001\u7f3a\u53e3\u3001\u6587\u737b DIS it be important to note that \u8a0e\u8ad6\uff1a\u8a0e\u8ad6\u672c\u8ad6\u6587\u8207\u524d\u4eba\u4e4b\u512a\u52a3\u7570\u540c TEX rest of paper structure as follow OWN in paper , we propose approach \u672c\u6587\uff1a\u63cf\u8ff0\u672c\u8ad6\u6587\u4e4b\u65b9\u6cd5\u3001\u7d50\u679c BKG follow NE ( CD ) , 3.2.4 \u7522\u751f\u6709\u6587\u6b65\u6a19\u793a\u4e4b\u8a13\u7df4\u8cc7\u6599 \u5728\u8a13\u7df4\u7684\u7b2c\u56db\u6b65\u9a5f\uff0c\u6211\u5011\u5229\u7528\u6709\u6a19\u8a18\u7684\u53e5\u578b\u53bb\u5339\u914d\u5927\u91cf\u8ad6\u6587\u7c21\u4ecb\u53e5\u5b50\uff0c\u4e26\u5c07\u53e5\u578b\u7684\u6587\u6b65 \u6a19\u8a3b\u5230\u53e5\u5b50\u4e0a\u9762\u3002\u5339\u914d\u7684\u539f\u5247\u662f\u6108\u9577\u7684\u53e5\u578b\u6108\u512a\u5148\u3002\u6211\u5011\u5229\u7528\u53e5\u578b\u4f86\u7522\u751f\u5927\u91cf\u6709\u6a19\u8a18\u6587 \u6b65\u7684\u53e5\u5b50\uff0c\u7528\u4ee5\u505a\u70ba\u4e4b\u5f8c\u6a21\u7d44\u7684\u8a13\u7df4\u8cc7\u6599\u3002\u8868 2 \u70ba\u5339\u914d\u6210\u529f\u7684\u53e5\u5b50\u7684\u7bc4\u4f8b\u3002\u9019\u500b\u968e\u6bb5\u7684 \u6a19\u8a3b\u7bc4\u570d\u662f\u55ae\u53e5\u3002 \u8868 2.\u53e5\u578b\u5c0d\u61c9\u53e5\u5b50\u7684\u7bc4\u4f8b \u6587\u6b65 \u53e5\u578b \u5339\u914d\u53e5\u5b50 TEX in section , we review work In the next section, we will first review some related works. BKG in year , there be In recent years, there has been a rapid growth of interest in the sociological study of childhood. OWN in paper , we propose approach In this paper, we propose a novel unsupervised approach to query segmentation, an important task in Web search. \u9ec3\u51a0\u8aa0 \u7b49 3.2.5 \u9644\u52a0\u8a13\u7df4\u8cc7\u6599\u4e4b\u7279\u5fb5\u503c \u5728\u8a13\u7df4\u7684\u7b2c\u4e94\u968e\u6bb5\uff0c\u6211\u5011\u8981\u9644\u52a0\u7279\u5fb5\u503c\u5230\u8a13\u7df4\u8cc7\u6599\u4ee5\u7528\u4f86\u8a13\u7df4\u6a19\u8a18\u6587\u6b65\u6a21\u578b\u3002\u6211\u5011\u5f9e\u53e5 \u5b50\u4e2d\u6240\u62bd\u51fa N \u9023\u8a5e\u7279\u5fb5\u503c\u3002\u8868 3 \u70ba N \u9023\u8a5e\u7279\u5fb5\u503c\u7684\u4f8b\u5b50\u3002\u70ba\u4e86\u8b93\u7279\u5fb5\u503c\u66f4\u80fd\u53cd\u61c9\u6587 \u6b65\uff0c\u6211\u5011\u4e5f\u52a0\u5165\u8a5e\u985e\u3001\u8a9e\u610f\u5206\u985e(Word class)\u7684\u7279\u5fb5\u503c\u3002\u6211\u5011\u5229\u7528 Teufel(1999)\u4e2d \u4eba\u5de5\u7de8\u8f2f\u7684\u4e00\u7d44\u5b78\u8853\u8ad6\u6587\u7684\u5206\u985e\u8a5e\u5f59\u3002\u8868 4 \u70ba\u6211\u5011\u6240\u4f7f\u7528\u7684 \u8a9e\u610f\u5206\u985e(Word class)\u7684 \u7279\u5fb5\u503c\u3002 \u8868 3. \u8f38\u5165\u53e5\"In this paper , we will describe a method \u2026\"\u7684 N \u9023\u8a5e\u7279\u5fb5\u503c N-gram Features Surface unigram in this paper we will describe a method Surface bigram in_this this_paper paper_, ,_we we_will will_describe describe_a a_method Lemma unigram in this paper we will describe a method Lemma bigram in_this this_paper paper_, ,_we we_will will_describe describe_a a_method Chunk head unigram in paper we describe method Chunk head bigram in_paper paper_, ,_we we_describe describe_method \u8868 4. \u5206\u985e\u8a5e\u985e\u96c6\u7bc4\u4f8b \u8a5e\u985e\u540d\u7a31 \u8a5e\u6027 \u8a5e\u5f59 AFFECT v afford, believe, decide, feel, hope, imagine, regard, trust, think COMPARISON v compare, compete, evaluate, test TEXT n paragraph, section, subsection, chapter 3.2.6 \u8a13\u7df4\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b \u76ee\u524d\u6709\u8a31\u591a\u6a5f\u5668\u5b78\u7fd2\u65b9\u6cd5\u53ef\u4ee5\u8655\u7406\u5206\u985e\u7684\u554f\u984c\u3002\u57fa\u672c\u7684\u76e3\u7763\u5f0f\u7684\u65b9\u6cd5\u9700\u8981\u6b63\u78ba\u7684\u5206\u985e\u8cc7 \u8a0a\uff0c\u975e\u76e3\u7763\u5f0f\u65b9\u6cd5\u5247\u4e0d\u9700\u8981\u6709\u6b63\u78ba\u7b54\u6848\u3002\u5728\u672c\u7814\u7a76\u4e2d\uff0c\u6211\u5011\u63a1\u7528\u76e3\u7763\u5f0f\u8a13\u7df4\u65b9\u6cd5\uff0c\u4f46\u662f \u6211\u5011\u4e26\u4e0d\u76f4\u63a5\u4eba\u5de5\u6a19\u8a3b\u6b63\u78ba\u7b54\u6848\u3002\u6211\u5011\u900f\u904e\u6a19\u8a3b\u5c11\u91cf\u53e5\u578b\uff0c\u9593\u63a5\u5730\u81ea\u52d5\u7522\u751f\u5927\u91cf\u7684\u6a19\u8a18 \u53e5\u5b50\uff0c\u4f5c\u70ba\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u65b9\u6cd5\u6240\u9700\u7684\u8a13\u7df4\u8cc7\u6599\uff0c\u4e26\u4f7f\u7528\u6700\u5927\u71b5\u6a21\u578b(Maximum Entropy, ME)\u4f86\u8a13\u7df4\u6587\u6b65\u5206\u985e\u5668\u3002 \u8a13\u7df4\u5b8c\u6210\u5f8c\uff0c\u6211\u5011\u5c31\u904b\u7528\u6b64\u4e00\u5206\u985e\u5668\uff0c\u5c07\u8a9e\u6599\u5eab\u5167\u6240\u6709\u7684\u8ad6\u6587\u53e5\u5b50\uff0c\u52a0\u4ee5\u5206\u985e\uff0c\u6a19 \u8a3b\u4e0a\u9069\u7576\u7684\u6587\u6b65\u3002\u4e4b\u5f8c\uff0c\u6211\u5011\u5c31\u53ef\u4ee5\u904b\u7528\u9019\u4e9b\u9644\u6709\u6587\u6b65\u6a19\u7c64\u7684\u53e5\u5b50\uff0c\u4f86\u7d71\u8a08\u5404\u7a2e\u6587\u6b65\u7684 \u5e38\u898b N \u9023\u8a5e\u3002\u4e4b\u5f8c\uff0cWriteAhead \u7cfb\u7d71\u5728\u8f14\u52a9\u5beb\u4f5c\u6642\uff0c\u5c07\u53c3\u7167\u4f7f\u7528\u8005\u8a2d\u5b9a\u7684\u6587\u6b65\uff0c\u4e26\u6839 \u64da\u8f38\u5165\u7684\u5167\u5bb9\uff0c\u67e5\u8a62\u9069\u7576\u7684\u7247\u8a9e\u63d0\u4f9b\u7d66\u5b78\u7fd2\u8005\u53c3\u8003\u3002 \u5b78\u8853\u8ad6\u6587\u7c21\u4ecb\u7684\u81ea\u52d5\u6587\u6b65\u5206\u6790\u8207\u5beb\u4f5c\u63d0\u793a 39 4. \u5be6\u9a57\u8207\u7d50\u679c \u6211\u5011\u8a2d\u8a08 WriteAhead \u7684\u521d\u8877\uff0c\u662f\u70ba\u4e86\u63d0\u793a\u4f7f\u7528\u8005\u63a5\u8457\u53ef\u4ee5\u5beb\u7684\u6578\u500b\u5b57\u8a5e\uff0c\u4ee5\u8f14\u52a9\u5b78\u7fd2 \u8005\u5beb\u4f5c\u5b78\u8853\u8ad6\u6587\u7684\u300c\u7c21\u4ecb\u300d\u3002\u56e0\u6b64\uff0c\u6211\u5011\u64f7\u53d6\u7d93\u904e\u5be9\u67e5\u3001\u7de8\u8f2f\u7684\u7a0b\u5e8f\uff0c\u767c\u8868\u7684\u5b78\u8853\u8ad6\u6587\uff0c \u4f86\u5be6\u4f5c\u6211\u5011\u63d0\u51fa\u7684\u65b9\u6cd5\uff0c\u4ee5\u53ca\u958b\u767c\u5beb\u4f5c\u8f14\u52a9\u7cfb\u7d71\u3002\u672c\u7bc0\u4e2d\uff0c\u6211\u5011\u63cf\u8ff0\u6a21\u7d44\u8a13\u7df4\u7684\u5be6\u9a57\u8a2d \u5b9a(\u7b2c 4.1 \u7bc0)\uff0c\u4ee5\u53ca\u521d\u6b65\u5be6\u9a57\u7684\u6548\u80fd\u8a55\u4f30\u8207\u7d50\u679c(\u7b2c 4.2 \u7bc0)\u3002 4.1 \u5be6\u9a57\u8a2d\u5b9a \u6211 \u5011 \u5f9e \u5bc6 \u897f \u6839 \u5927 \u5b78 \u7684 \u8a08 \u7b97 \u8a9e \u8a00 \u5b78 \u53ca \u8cc7 \u8a0a \u6aa2 \u7d22 \u7d44 ( Computational Linguistics And Information Retrieval Group, CLAIR) \u8a2d \u8a08 \u7dad \u8b77 \u7684 \u8a08 \u7b97 \u8a9e \u8a00 \u5b78 \u6703 ( Association for Computational Linguistic)\u6703\u8b70\u8207\u671f\u520a\u8ad6\u6587\u5178\u85cf\u7db2\u7ad9 ACL Anthology Network(AAN, clair.eecs.umich.edu/aan)\uff0c\u6211\u5011\u64f7\u53d6 AAN \u5b78\u6703\u7684\u6703\u8b70\u8207\u671f\u520a\u8ad6\u6587\uff0c\u5171\u56db\u842c\u591a\u7bc7\u7684\u8ad6\u6587 \u7684\u6587\u5b57\u6a94\u6848\u3002 \u9019\u4e9b\u6a94\u6848\u4e3b\u8981\u662f\u7531 PDF \u683c\u5f0f\u7684\u6a94\u6848\uff0c\u900f\u904e\u8f49\u6a94(\u985e\u4f3c\u65bc OCR \u8fa8\u8b58)\u6240 \u5f97\u5230\u7684\u6587\u5b57\u6a94\u6848\u3002\u56e0\u6b64\uff0c\u9019\u4e9b\u6a94\u6848\u6709\u8457\u5404\u5f0f\u7684\u96dc\u8a0a\uff0c\u50cf\u662f\u6b98\u7559\u7684\u63db\u884c\u9023\u5b57\u7b26\u865f\u3001\u55ae\u5b57\u8fa8 \u8b58\u932f\u8aa4\u7b49\u3002 \u6211\u5011\u900f\u904e\u8a2d\u8a08\u53ca\u5206\u6790\u898f\u5247\uff0c\u8a2d\u5b9a\u7c21\u55ae\u7684\u689d\u4ef6\uff0c\u8fa8\u8b58\u51fa\u7bc0\u7684\u6a19\u984c\uff0c \u4e26\u6311\u9078\u4e86 \u6a19\u793a\u5f88\u6e05\u695a\u7684\u8ad6\u6587\u5c07\u8fd1\u4e00\u842c\u7bc7\u3002\u4e4b\u5f8c\uff0c\u6211\u5011\u6839\u64da\u6a19\u984c\u7684\u7de8\u865f\uff0c\u6a19\u984c\u7684\u5167\u5bb9\uff0c\u62bd\u53d6\u300c\u7c21\u4ecb\u300d \u90e8\u5206\u4f86\u505a\u70ba\u7814\u7a76\u7684\u8a13\u7df4\u8cc7\u6599\uff0c\u4ee5\u53ca\u7cfb\u7d71\u958b\u767c\u7684\u8cc7\u6599\u3002 \u8868 5. \u6709\u5339\u914d\u53e5\u578b\u4e4b\u53e5\u5b50\u6587\u6b65\u5206\u5e03\u60c5\u5f62 \u6587\u6b65 \u53e5\u6578 BKG 3,333 OWN 7,199 DIS 1,572 TEX 5,687 \u7e3d\u8a08 17,791 \u6211\u5011\u9010\u7bc7\u8655\u7406\u9019\u4e00\u842c\u7bc7\u8ad6\u6587\u7c21\u4ecb\u3002\u6211\u5011\u5229\u7528 Python/NLTK 2 \u7684\u5206\u5272\u82f1\u6587\u53e5\u5b50\u3001\u8a5e\u5f59 \u7684\u5de5\u5177\uff0c\u5c07\u4e00\u7bc7\u7bc7\u8ad6\u6587\u5206\u5272\u6210\u53e5\u5b50\uff0c\u518d\u5c07\u53e5\u5b50\u5206\u5272\u6210\u8a5e\u5f59\u8207\u6a19\u9ede(tokens)\u3002\u6709\u4e86\u53e5\u5b50 \u8207\u8a5e\u5f59\u5f8c\uff0c\u6211\u5011\u63a5\u8457\u4f7f\u7528 Genia Tagger 3 \u6a19\u8a3b\u8a5e\u6027\u8207\u57fa\u5e95\u7247\u8a9e (base phrase \u6216 chunks) \u3002 \u4e4b\u5f8c\uff0c\u7576\u6240\u6709\u7684\u7dd2\u8ad6\u55ae\u5b57\u90fd\u88ab\u65b7\u8a5e\u548c\u6a19\u8a18\u8a5e\u6027\u4ee5\u53ca\u5340\u584a\u5f8c\uff0c\u6211\u5011\u5229\u7528\u7d71\u8a08\u65b9\u6cd5\u7372\u5f97\u82e5\u5e72 \u7684\u53e5\u578b\u3002\u6211\u5011\u4eba\u5de5\u7684\u6311\u9078\u4e86\u4e94\u767e\u500b\u53e5\u578b\u5f8c\uff0c\u624b\u52d5\u6ffe\u6389\u6587\u6b65\u7279\u6027\u4e0d\u660e\u986f\u5f97\u7684\u7247\u8a9e\u4e26\u628a\u5269\u4e0b \u7684\u53e5\u578b\u90fd\u6a19\u4e0a\u6587\u6b65\uff0c\u5269\u4e0b\u8fd1\u7d04\u56db\u767e\u500b\u6709\u6587\u6b65\u6a19\u8a18\u7684\u53e5\u578b\u3002\u6211\u5011\u5728\u5229\u7528\u9019\u4e9b\u6a19\u8a18\u904e\u7684\u53e5\u578b \u53bb\u5339\u914d\u4e00\u842c\u7bc7\u7684\u8ad6\u6587\u7c21\u4ecb\u3002\u6211\u5011\u5f97\u5230\u5927\u7d04\u4e00\u842c\u516b\u5343\u500b\u53e5\u5b50\uff0c\u5176\u6587\u6b65\u7684\u5206\u4f48\u5982\u8868 5 \u6240\u793a\u3002 \u6a19\u8a3b\u6a21\u7d44\u3002 system goal in paper be to solution be to paper provide in CD , we present approach finally , we draw conclusion paper organize as follow CD present work \u65bc\u8cc7\u6599\uff0c\u672c\u7cfb\u7d71\u61c9\u8a72\u5c0d\u975e\u8cc7\u8a0a\u9818\u57df(\u4f8b\u5982\u6587\u5b78\u3001\u7ba1\u7406\u5b78\u3001\u6559\u80b2\u5b78)\u7684\u9069\u7528\u6027\u61c9\u8a72\u4e0d\u662f\u5f88 in paper , we describe goal of paper be to therefore , we we demonstrate follow experiment discussion present in CD CD present result \u696d\u9818\u57df\u7279\u6b8a\u6027\u7684\u5f71\u97ff\u3002\u4f46\u662f\uff0c\u500b\u5225\u9818\u57df\u8868\u9054\u7684\u65b9\u5f0f\u5728\u7528\u5b57\u9063\u8a5e\u4ecd\u7136\u6709\u4e0d\u5c0f\u7684\u5dee\u7570\uff0c\u53d7\u9650 in paper , we show how in paper , we investigate purpose be to part of paper organize as CD present result of work discuss in CD CD describe system paper describe CRFs \u3002\u672c\u8ad6\u6587\u6240\u63d0\u51fa\u7684\u65b9\u6cd5\uff0c\u662f\u57fa\u65bc\u8de8\u9818\u57df\u7684\u8ad6\u6587\u4fee\u8fad\u7814\u7a76\uff0c\u61c9\u8a72\u4e0d\u6703\u53d7\u4e0d\u540c\u5b78\u8853\u5c08 in study , we focus on goal of work be to paper describe system follow result finally , in CD finally , we aim of \u672c \u8ad6 \u6587 \u6240 \u4f7f \u7528 \u7684 \u5206 \u985e \u5668 \u662f Maximal Entropy \uff0c \u672a \u4f86 \u4e5f \u5c07 \u8003 \u616e \u63a1 \u7528 SVM \u6216 \u662f in paper , we focus on in paper , we propose in paper we focus be rest of paper organise as section present and discuss in rest of paper plan of paper \u518d\u5c07\u6a19\u8a18\u597d\u7684\u53e5\u5b50\u9644\u52a0\u4e0a\u7279\u5fb5\u503c N-gram\u3001\u8a5e\u8a9e\u5206\u985e\u5f8c\uff0c\u8b93 ME \u6a21\u7d44\u505a\u8a13\u7df4\uff0c\u7372\u5f97\u6587\u6b65 \u9ec3\u51a0\u8aa0 \u7b49 \u6211\u5011\u85c9\u7531\u8a13\u7df4\u6240\u5f97\u7684\u6587\u6b65\u6a19\u8a3b\u6a21\u7d44\uff0c\u5c0d\u4e00\u842c\u7bc7\u7c21\u4ecb\u4e2d\u7684\u6bcf\u4e00\u53e5\u9032\u884c\u6587\u6b65\u6a19\u8a3b\u3002\u6700\u5f8c \u6211\u5011\u7d71\u8a08\u5404\u7a2e\u6587\u6b65\u4e2d\u7684 N \u9023\u8a5e\u8cc7\u8a0a\uff0c\u6211\u5011\u7e7c\u800c\u5c07\u4e00\u842c\u591a\u7bc7\u7c21\u4ecb\u5167\u7684\u53e5\u5b50\uff0c\u9010\u53e5\u505a\u6587\u6b65\u7684 \u5206\u985e\uff0c\u904b\u7528\u65bc WriteAhead \u5beb\u4f5c\u8f14\u52a9\u7cfb\u7d71\u3002 4.2 \u8a55\u4f30\u8207\u8a0e\u8ad6 \u5982\u524d\u6240\u8ff0\uff0cWriteAhead \u7684\u8a2d\u8a08\u76ee\u6a19\u662f\u8f14\u52a9\u5b78\u7fd2\u8005\u5beb\u4f5c\u5b78\u8853\u8ad6\u6587\u7684\u300c\u7c21\u4ecb\u300d\uff0c\u6240\u4ee5\u61c9\u8a72\u8a55 \u4f30\u5404\u7a2e\u5beb\u4f5c\u60c5\u5883\u4e0b\uff0c\u4f7f\u7528\u8005\u89ba\u5f97 WriteAhead \u7684\u63d0\u793a\uff0c\u662f\u5426\u6709\u52a9\u65bc\u5beb\u4f5c\u51fa\u66f4\u597d\u7684 \u300c\u7c21\u4ecb\u300d \u3002 \u7136\u800c\uff0c\u4e00\u822c\u800c\u8a00\uff0c\u51e1\u662f\u6d89\u53ca\u4f7f\u7528\u8005\u7684\u8a55\u4f30\u90fd\u662f\u975e\u5e38\u56f0\u96e3\u3002\u9000\u800c\u6c42\u5176\u6b21\uff0c\u6211\u5011\u76ee\u524d\u50c5\u91dd\u5c0d \u6587\u6b65\u5206\u985e\u5668\u90e8\u5206\uff0c\u8a55\u4f30\u5176\u5206\u985e\u6b63\u78ba\u6027\u3002\u7531\u65bc\u8ad6\u6587\u7684\u6587\u6b65\u662f\u4f9d\u5e8f\u63a8\u79fb\uff0c\u6240\u4ee5\u6211\u5011\u91dd\u5c0d\u300c\u7c21 \u4ecb\u300d \u7684\u6574\u500b\u7bc0\uff0c\u4f86\u8a55\u4f30\u6587\u6b65\u7684\u6a19\u8a3b\u662f\u5426\u6b63\u78ba\u3002 \u8868 6. \u7e3d\u5171 50 \u7bc7\u7c21\u4ecb\u4e4b\u53e5\u5b50\u6a19\u793a\u6587\u6b65\u8207\u9810\u6e2c\u6587\u6b65\u8207\u9810\u6e2c\u6b63\u78ba\u7387 \u6587\u6b65 \u6a19\u793a\u53e5\u6578 \u9810\u6e2c\u53e5\u6578 \u6b63\u78ba\u53e5\u6578 \u7cbe\u78ba\u7387 BKG 621 470 402 .86 OWN 238 259 144 .56 DIS 312 461 241 .52 TEX 117 98 75 .76 \u7e3d\u8a08 1,288 1,288 862 .67 \u70ba\u4e86\u9054\u6210\u80fd\u81ea\u52d5\u7684\u70ba\u8ad6\u6587\u7c21\u4ecb\u53e5\u5b50\u6a19\u8a3b\u6587\u6b65\u6b64\u4e00\u76ee\u6a19\uff0c\u6211\u5011\u5f9e ACL Anthology Network \u4e2d\u96a8\u6a5f\u6311\u9078\u4e94\u5341\u7bc7\u8ad6\u6587\u7c21\u4ecb\u7684\u53e5\u5b50\uff0c\u505a\u70ba\u6211\u5011\u6587\u6b65\u6a19\u8a3b\u6a21\u7d44\u7684\u8a55\u4f30\u8cc7\u6599\u3002\u8868 6 \u986f \u793a\u8a55\u4f30\u7684\u7d50\u679c\u3002\u6574\u9ad4\u7684\u6587\u6b65\u9810\u6e2c\u6b63\u78ba\u7387 67%\uff0c\u9084\u6709\u6539\u5584\u7684\u7a7a\u9593\u3002\u5c31\u500b\u5225\u7684\u6587\u6b65\u4f86\u770b\uff0c\u80cc \u666f\u6587\u6b65 ( BKG)\u7684\u6b63\u78ba\u7387\u9054 86% \u800c\u6587\u8108\u6587\u6b65(TEX)\u9054 76%\uff0c\u9019\u53ef\u80fd\u662f\u56e0\u70ba\u80cc\u666f\u3001 \u6587\u8108\u6587\u6b65\u5169\u8005\u90fd\u6709\u6bd4\u8f03\u56fa\u5b9a\u7684\u8868\u9054\u65b9\u5f0f\u3002\u76f8\u5c0d\u7684\uff0c\u672c\u8ad6\u6587(OWN)\u3001\u8a0e\u8ad6( DIS)\u5169\u7a2e \u6587\u6b65\u7684\u7cbe\u78ba\u7387\u50c5\u50c5\u7565\u9ad8\u65bc 50%\uff0c\u9019\u7576\u7136\u662f\u56e0\u70ba\u8868\u9054\u7684\u65b9\u5f0f\u6bd4\u8f03\u5206\u6b67\uff0c\u4e0d\u6613\u900f\u904e\u5e38\u898b\u53e5\u578b \u4f86\u52a0\u4ee5\u638c\u63e1\uff0c\u672a\u4f86\u53ef\u80fd\u9084\u9700\u8981\u767c\u6398\u6bd4\u8f03\u6709\u6548\u7684\u7279\u5fb5\u503c\u3002 \u500b\u5225\u53e5\u5b50\u7684\u5206\u985e\u6b63\u78ba\u7387\u4e26\u4e0d\u9ad8\uff0c\u9019\u53ef\u80fd\u6b78\u548e\u65bc\u5e7e\u500b\u539f\u56e0\u3002\u9996\u5148\uff0c\u6a19\u8a3b\u8cc7\u6599\u592a\u5c11\uff0c\u800c \u4e14\u6a19\u8a3b\u7684\u6b63\u78ba\u6027\u4e5f\u4e0d\u662f\u975e\u5e38\u7406\u60f3\u3002\u53e6\u5916\uff0c\u8868\u9054\u540c\u4e00\u985e\u7684\u6587\u6b65\uff0c\u7528\u5b57\u9063\u8a5e\u7684\u5dee\u7570\u6027\u5f88\u5927\uff0c \u5f88\u96e3\u7528\u6709\u9650\u7684\u8cc7\u6599\u4f86\u638c\u63e1\uff0c\u76f8\u53cd\u5730\u5b57\u8a5e\u4e5f\u6709\u4e0d\u5c0f\u7684\u8a5e\u5f59\u8a9e\u610f\u6b67\u7fa9\u3002 \u96d6\u7136\u500b\u5225\u53e5\u5b50\u7684\u5206\u985e\u6b63\u78ba\u6027\u4e0d\u7406\u60f3\uff0c\u6211\u5011\u89c0\u5bdf\u7d71\u8a08\u5f8c\u7684\u5404\u5206\u985e\u4e4b\u9ad8\u983b N \u9023\u8a5e\u9084\u7b97 that in paper , we study we demonstrate that purpose of as follow finally , CD conclude paper paper organise as follow CD show result \u8005\u7684\u6548\u679c\u3002\u4e0d\u904e\u6211\u5011\u8a8d\u70ba\uff0c\u9ad8\u983b N \u9023\u8a5e\u7684\u7cbe\u78ba\u7387\u53ef\u80fd\u9060\u9ad8\u65bc\u6587\u6b65\u6a19\u793a\u7684\u7cbe\u78ba\u7387\u3002 in particular , we show in work , we use paper focus on remainder of paper structure in CD we present experiment in section CD , CD describe method paper present \u5408\u7406\u3002\u53d7\u9650\u65bc\u6642\u9593\uff0c\u6211\u5011\u5c1a\u672a\u8a55\u4f30 WriteAhead \u904b\u7528\u5404\u5206\u985e\u9ad8\u983b N \u9023\u8a5e\uff0c\u5c0d\u65bc\u63d0\u793a\u4f7f\u7528 5. \u7d50\u8ad6 \u5c0d\u65bc\u5982\u4f55\u6539\u5584\u6211\u5011\u6240\u63d0\u51fa\u7684\u7cfb\u7d71\uff0c\u6211\u5011\u9810\u898b\u8a31\u591a\u53ef\u80fd\u7684\u672a\u4f86\u7814\u7a76\u65b9\u5411\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u904b\u7528 \u65e2\u6709\u7684\u81ea\u7136\u8a9e\u8a00\u8655\u7406\u6280\u8853\uff0c\u64f7\u53d6\u66f4\u5177\u6548\u679c\u7684\u7279\u5fb5\u503c\uff0c\u4f86\u63d0\u5347\u6587\u6b65\u5206\u985e\u7684\u6b63\u78ba\u7387\u3002\u4f8b\u5982\uff0c \u6211\u5011\u53ef\u4ee5\u81ea\u52d5\u7522\u751f\u5beb\u4f5c\u6587\u9ad4\u4e4b\u5206\u985e\u8a5e\u5f59\u7fa4\u3002\u4e26\u4e14\uff0c\u6839\u64da\u5206\u985e\u8a5e\u5f59\u7fa4\uff0c\u64f7\u53d6\u8a5e\u7fa4\u5f0f\u7684\u5e38\u898b \u6a23\u677f(class-based patterns)\uff0c\u7528\u4f86\u5e6b\u52a9\u5206\u985e\u7684\u6b63\u78ba\u6027\uff0c\u4ee5\u53ca\u63d0\u4f9b\u5bcc\u542b\u8cc7\u8a0a\u7684\u5beb\u4f5c\u63d0\u793a\u3002 \u53e6\u5916\u4e00\u500b\u6709\u6f5b\u529b\u7684\u7814\u7a76\u65b9\u5411\uff0c\u662f\u8b93\u4f7f\u7528\u8005\u5728\u53e6\u4e00\u500b\u6587\u5b57\u6846\uff0c\u8f38\u5165\u6bcd\u8a9e(\u5982\u4e2d\u6587\u3001\u65e5\u6587) \u8349\u7a3f\uff0c\u800c\u7cfb\u7d71\u53c3\u8003\u9019\u4e9b\u6bcd\u8a9e\u8349\u7a3f\uff0c\u4f86\u8abf\u6574\u63d0\u793a\u7684\u82f1\u6587\u53e5\u578b\u8207\u7247\u8a9e\u3002\u53e6\u5916\uff0c\u6211\u5011\u4e5f\u53ef\u4ee5\u8b93 \u4f7f\u7528\u8005\u9078\u53d6\u90e8\u5206\u6c92\u6709\u628a\u63e1\u7684 2-5 \u500b\u5b57\uff0c\u7cfb\u7d71\u63d0\u793a\u6b63\u78ba\u6216\u932f\u8aa4\u7684\u6a5f\u7387\uff0c\u4ee5\u53ca\u5176\u4ed6\u53ef\u4ee5\u66ff\u63db \u7684\u8868\u9054\u65b9\u5f0f\u3002 \u7e3d\u800c\u8a00\u4e4b\uff0c\u6211\u5011\u4ecb\u7d39\u4e86\u4e00\u5957\u65b9\u6cd5\uff0c\u80fd\u8655\u7406\u6240\u641c\u96c6\u5230\u7684\u5b78\u8853\u8ad6\u6587\uff0c\u5c07\u6bcf\u4e00\u500b\u53e5\u5b50\u6a19\u793a \u4e0a\u9069\u7576\u7684\u6587\u6b65(move)\uff0c\u4e26\u7d71\u8a08\u5404\u985e\u6587\u6b65\u7684\u5e38\u898b\u7247\u8a9e\uff0c\u85c9\u4ee5\u5e6b\u52a9\u82f1\u6587\u975e\u5176\u6bcd\u8a9e\u5b78\u751f\uff0c\u5beb \u4f5c\u5b78\u8853\u8ad6\u6587\u3002 \u6211\u5011\u7684\u65b9\u6cd5\u6d89\u53ca\u64f7\u53d6\u5e38\u898b\u5beb\u4f5c\u53e5\u578b\u3001\u6a19\u793a\u53e5\u578b\u7684\u6587\u6b65\u3001\u7522\u751f\u5927\u91cf\u5df2\u6a19\u793a\u6587 \u6b65\u7684\u53e5\u5b50\u4ee5\u53ca\u7279\u5fb5\u503c\uff0c\u4f5c\u70ba\u8a13\u7df4\u8cc7\u6599\u4f86\u958b\u767c\u6587\u6b65\u5206\u985e\u5668\u3002\u6211\u5011\u85c9\u7531\u6b64\u4e00\u5206\u985e\u5668\uff0c\u9810\u6e2c\u53e5 \u5b50\u7684\u6587\u6b65\u3002\u6211\u5011\u63d0\u51fa\u4e00\u500b\u96db\u578b\u7cfb\u7d71 WriteAhead\uff0c\u61c9\u7528\u5206\u985e\u7684\u53e5\u5b50\u8207\u5e38\u898b\u7247\u8a9e\u7684\u8cc7\u6599\uff0c\u63d0 \u793a\u5b78\u7fd2\u8005\uff0c\u5982\u4f55\u5beb\u4f5c\u5404\u7a2e\u6587\u6b65\u7684\u53e5\u5b50\u3002 \u81f4\u8b1d\u8a5e \u672c\u7814\u7a76\u627f\u8499\u79d1\u6280\u90e8\u88dc\u52a9\u7814\u7a76\u7d93\u8cbb\uff0c\u8a08\u756b\u6a19\u865f NSC 100-2511-S-007 -005 -MY3\u3002 \u53c3\u8003\u6587\u737b Anthony, L., & Connor, U., & Mauranen, A. (1999). Linguistic Analysis of Grant Proposals: European Union Research Grants. Della Pietra, S., Della Pietra, V., Lafferty, J., Technol, R., & Brook, S. (1997). Inducing features of random fields. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 19(4), 380-393. Edmundson, H. P. (1969). New Methods in Automatic Extracting. Journal of the Association for Computing: Machinery, 16(2), 264-285. (2007). Using argumentation to extract key sentences from biomedical bstracts. International journal of medical informatics, 76(2), 195-200. Shimbo, M., Yamasaki, T., & Matsumoto, Y. (2003). Using sectioning information for text retrieval: a case study with the MEDLINE abstracts. Swales, J.M. (1990). Genre analysis: English in Academic and Research Settings. Cambridge University Press. Teufel, S. (1999). Argumentative Zoning: Information Extraction from Scientific Text. PhD thesis, University of Edinburgh. Teufel, S., & Moens, M. (2002). Summarizing Scientific Articles: Experiments with Relevance and Rhetorical Status. Computational Linguistics, 28(4), 409-445. \u5b78\u8853\u8ad6\u6587\u7c21\u4ecb\u7684\u81ea\u52d5\u6587\u6b65\u5206\u6790\u8207\u5beb\u4f5c\u63d0\u793a 43 \u9644\u9304 A \u6574\u5408\u5e38\u898b\u53e5\u578b\u7684\u5beb\u4f5c\u6a23\u677f \u6211\u5011\u64f7\u53d6\u5e38\u898b\u53e5\u578b\u6a19\u793a\u6587\u6b65\u4e4b\u5f8c\uff0c\u767c\u73fe\u8a31\u591a\u53e5\u578b\u5f88\u985e\u4f3c\uff0c\u53ea\u6709\u5c11\u6578\u7684\u5e7e\u500b\u5b57\u8b8a\u52d5\u3002\u6211\u5011 \u53ef\u5c07\u9019\u4e9b\u53e5\u578b\u805a\u96c6\u8d77\u4f86\uff0c\u6b78\u7d0d\u6574\u5408\u6210\u70ba\u6b63\u898f\u5f0f\u6a23\u677f(regular expression patterns)\u3002\u9019\u4e9b \u6a23\u677f\u907f\u514d\u7f85\u5217\u8a31\u591a\u53e5\u578b\u7684\u4e0d\u4fbf\uff0c\u4e00\u76ee\u4e86\u7136\u2500\u2500\u65e2\u4ee3\u8868\u4e86\u5beb\u4f5c\u7684\u5e38\u614b\uff0c\u4e5f\u5448\u73fe\u4e86\u5404\u7a2e\u8b8a\u5316\u3002 \u904b\u7528\u5728\u6559\u5b78\u4e0a\u8b93\u5b78\u751f\u5b78\u7fd2\u5f88\u6709\u6548\u679c\uff0c\u5beb\u4f5c\u6642\u4e5f\u5bb9\u6613\u52a0\u4ee5\u6a21\u4eff\u3001\u6539\u5beb\u3002 \u4f8b\u5982\uff0c\u5f9e\u9644\u9304 B \u4e2d\u6211\u5011\u53ef\u4ee5\u770b\u5230\u4e0b\u9762\u5de6\u908a\u9019\u4e9b\u548c\u6642\u9593\u6709\u95dc\u7684\u53e5\u578b\u3002\u7d93\u904e\u89c0\u5bdf\u8207\u6b78 \u7d0d\uff0c\u6211\u5011\u53ef\u4ee5\u5f97\u5230\u4e0b\u9762\u53f3\u908a\u7684\u6a23\u677f\u53ca\u5176\u8b8a\u5316\u578b\uff1a \u9ec3\u51a0\u8aa0 \u7b49 \u9644\u9304 B \u5404\u7a2e\u6587\u6b65\u7684\u5e38\u898b\u53e5\u578b B.1 \u80cc\u666f\u6587\u6b65 follow NE ( CD ) , NE ( CD ) show that NE ( CD ) demonstrate that NE ( CD ) propose model it be , however , there be , however , to knowledge , there be to good of knowledge , in case , however , NE ( CD ) present NE ( CD ) describe however , in case , to knowledge , this be collection comprise CD in practice , however , recognition ( NE ) be NE ( CD ) propose as matter of fact , on hand , approach currently , there be this , however , first of all , however , for language approach , however , research support by NE however , there be however , while study show that difficulty be that currently , system there be also most of method challenge be that recently , model however , they at present , in general , it know that as alternative , over year , this be important much of work over decade , however , if however , unlike recently , method in year , it observe that they show that there be work however , when to date , most of system to knowledge , this be task it recognize that however , since in decade , however , study however , approach unfortunately , difficulty be problem with challenge be they describe currently , traditionally , in year while approach unlike method recently , recently B.2 \u300c\u672c\u8ad6\u6587\u300d\u6587\u6b65 in paper , we propose approach in paper , we present approach in paper , we present mothed in paper , we present system focus of paper be on goal of research be to in paper , we use purpose of paper be to in paper , we consider in paper , we describe in paper , we address in work we focus on in study , we paper address problem of to address problem , result show that approach in paper we present focus of paper be we propose that in study , we start with we hypothesize that hypothesis be that goal be motivation for in study in paper in CD , we present model next , in CD , finally CD conclude paper CD review work in work \u5b78\u8853\u8ad6\u6587\u7c21\u4ecb\u7684\u81ea\u52d5\u6587\u6b65\u5206\u6790\u8207\u5beb\u4f5c\u63d0\u793a 45 method in paper , we argue that in paper , we propose model in paper we focus on in paper , we present in paper we show that in paper we describe system work present in paper in paper , we we also show that paper propose method for in paper we discuss in paper we investigate in paper we propose to achieve goal , in paper , i thus , method finally , result experiment show that work focus on goal be to claim be that result indicate that therefore , method in work , evaluation show that result show that we evaluate approach we show that B.3\u300c\u8a0e\u8ad6\u300d\u6587\u6b65 it be important to note that this be due to fact that contribution of paper be as follow however , we believe that advantage of approach be that contribution of work be : in order to do this view express endorse by sponsor as it turn out , reason for this be that it be worth note that contribution of paper be : to overcome problem , for example , name in particular , it in contrast , model it be obvious that it turn out that contribution of paper be reason for this be to knowledge , work we also show how in contrast , system first , it as result of contribution be : by contrast , in comparison , for reason , in practice , reason be that specifically , it this be problem this lead to as consequence , that be why intuition be that analysis show that this mean that we believe that in principle , on contrary , example show that difference be that in short , we then discuss unlike NE , it note that among them , in sum , this be because we note that this suggest that contribution be advantage of observation be we believe although approac remainder of paper organize as follow in CD , we describe model rest of paper structure as follow in CD we describe how paper structure as follow : in remainder of paper , in CD we discuss work we discuss work in CD in CD we discuss in what follow , result show in CD finally , CD present article organize as follow we then present CD describe model CD present method CD describe result CD discuss result 46 \u9ec3\u51a0\u8aa0 \u7b49 remainder of paper organise as follow in CD , we describe system rest of paper organize as follow outline of paper be as follow paper organize as follow : CD structure of paper be as follow in CD , we describe method paper organize as follow : in finally , we conclude in CD in CD , we describe corpus in CD , we review work organization of paper be as follow finally , CD present conclusion finally , in CD , in CD we present result for example , CD show in section of paper , paper organize as follow : in CD we show that we conclude paper in CD in rest of paper , finally , we present result in section , we describe CD show example of finally , CD conclude as we see , CD give overview of result report in CD result present in CD as we show , paper proceed as follow we conclude in CD result discuss in CD approach describe in CD in CD we introduce paper structure as follow in CD we describe conclusion draw in CD result give in CD after that , CD present evaluation structure of paper CD conclude paper CD report result CD describe algorithm CD present algorithm CD present experiment CD introduce model CD introduce method CD present model CD show example CD describe how CD describe experiment CD describe setup in section , CD show how CD describe work CD describe approach CD give result CD discuss work in section CD describe CD introduce CD conclude CD show CD detail CD explain CD present CD discuss 1. Introduction \u8a9e\u8a00\u6a21\u578b(Language Models, LM)\u5df2\u88ab\u5ee3\u6cdb\u5730\u4f7f\u7528\u65bc\u8a9e\u97f3\u8fa8\u8b58\u3001\u6a5f\u5668\u7ffb\u8b6f\u3001\u8cc7\u8a0a\u6aa2\u7d22\u4ee5\u53ca \u6587\u4ef6\u6458\u8981\u7b49\u5404\u7a2e\u4efb\u52d9\u4e4b\u4e2d\uff0c\u4e26\u6210\u70ba\u95dc\u9375\u7684\u7d44\u6210(Rosenfeld, 2000; Bellegarda, 2004)\u3002\u5728\u8a9e \u97f3\u8fa8\u8b58\u4efb\u52d9\u4e0a\uff0c\u5176\u4e3b\u8981\u7684\u529f\u80fd\u901a\u5e38\u662f\u85c9\u7531\u5df2\u89e3\u78bc\u7684\u6b77\u53f2\u8a5e\u5e8f\u5217(Word History)\u8cc7\u8a0a\u4f86\u9810\u6e2c Graetz, N. (1985). \u9ec3\u51a0\u8aa0 \u7b49 in work , we focus on in paper , we report on in paper , we show that aim of paper be to in paper , we explore in paper , we introduce result show that model in work , we result show that method aim be to in paper , in section , we review work we discuss result in CD in CD we present in section that we argue that \u4e0b\u4e00\u500b\u8a5e\u5f59(Upcoming Word)\u70ba\u4f55\u7684\u53ef\u80fd\u6027\u6700\u5927\uff0c\u4ee5\u5354\u52a9\u8a9e\u97f3\u8fa8\u8b58\u7cfb\u7d71\u5f9e\u773e\u591a\u6df7\u6dc6\u7684\u5019\u9078 B.4\u300c\u7d44\u7e54\u300d\u6587\u6b65 \u8a5e\u5e8f\u5217\u5047\u8a2d(Candidate Word Sequence Hypotheses)\u4e2d\u627e\u51fa\u6700\u6709\u53ef\u80fd\u7684\u7d50\u679c
\u7406\u60f3\uff0c\u9700\u8981\u53e6\u5916\u8490\u96c6\u8cc7\u6599\uff0c\u4f9d\u7167\u5b78\u79d1\u5efa\u7f6e\u4e0d\u540c\u7684\u7cfb\u7d71\u3002 in paper , we propose in paper we describe idea be towe evaluate
", "type_str": "table", "num": null, "html": null }, "TABREF14": { "text": "", "content": "
First spectral peak from the non-partitioned episodes (NP) and three
partitioned cry episodes with equal length (P1, P2, P3) in the term and
preterm infants
FSP (Hz)
GroupNPP1P2P3
TermMean182.07135.88184.79149.46
SD139.06113.24142.45119.31
PretermMean130.44104.35117.40139.14
SD71.7452.0667.3682.11
", "type_str": "table", "num": null, "html": null }, "TABREF17": { "text": "", "content": "
HFE (dB)
", "type_str": "table", "num": null, "html": null }, "TABREF19": { "text": "Richard Tzong-Han Tsai, and Wen-Lian Hsu. Money Order or Check payable to \"The Association for Computation Linguistics and Chinese Language Processing \" or \"\u4e2d\u83ef\u6c11\u570b\u8a08\u7b97\u8a9e\u8a00\u5b78\u5b78\u6703\" \u2027 E-mail\uff1aaclclp@hp.iis.sinica.edu.tw", "content": "
IJCLCLP 2014 Index-2
Joint Learning of Entity Linking Constraints Using a Markov-Logic Network; 19(1): 11-32 Dinh, Dien see Tran, Phuoc, 19(1): 1-10 Wu, Yi-An and Shu-Kai Hsieh. Public Opinion Toward CSSTA: A Text Mining Approach; 19(4): Publications of the Association for K see ASAHARA, Masayuki , 19(3): 1-24 KATO, Sachi \u4e2d\u83ef\u6c11\u570b\u8a08\u7b97\u8a9e\u8a00\u5b78\u5b78\u6703 Computational Linguistics and Chinese Language Processing \u76f8\u95dc\u51fa\u7248\u54c1\u50f9\u683c\u8868\u53ca\u8a02\u8cfc\u55ae 19-28 KONISHI, Hikari see ASAHARA, Masayuki , 19(3): 1-24 Ku, Tsun \u7de8\u865f \u66f8\u76ee \u6703 \u54e1 \u975e\u6703\u54e1 \u518a\u6578 \u91d1\u984d X Xue, Yu-Zhi AIR no.92-01, no. 92-04 (\u5408\u8a02\u672c) ICG \u4e2d\u7684\u8ad6\u65e8\u89d2\u8272 \u8207 AIR 1. A conceptual Structure for Parsing Mandarin--its see Zeng, Yi-Ching, 19(2): 17-32 see Tsai, Wei-Ho, 19(1): 55-68 Surface (US&EURP) (ASIA) VOLUME Frame and General Applications--NT$ 80 NT$ _____ _____AMOUNT
E Linking Databases using Matched Arabic Names; El-Shishtawy, Tarek 19(1): 33-54 H Hao, Po-Han Ssu-Cheng Chen, and Berlin Chen. Exploring Concept Information for Mandarin Large Vocabulary Continuous Speech Recognition; 19(4): 47-60 Hsiang, Jieh see Wang, Yu-Chun, 19(3): 25-38 Hsieh, Shu-Kai see Wu, Yi-An, 19(4): 19-28 Hsieh, Yu-Ming see Huang, Shu-Ling, 19(2): 33-52 Hsu, Hsiang-Ling see Huang, Guan-Cheng, 19(4): 29-46 Hsu, Wen-Lian see Dai, Hong-Jie, 19(1): 11-32 Y Yen, Tzu-Hsi see Huang, Guan-Cheng, 19(4): 29-46 V-N \u8907\u5408\u540d\u8a5e\u8a0e\u8ad6\u7bc7 \u8207V-R \u8907\u5408\u52d5\u8a5e\u8a0e\u8ad6\u7bc7 L see Huang, Shu-Ling, 19(2): 33-52 Lin, Su-Chu Lin, Yu-Yang and Chia-Hui Chang. POI Extraction from the Web: Store Name Recognition and Address Matching; 19(4): 1-18 2. no.92-02, no. 92-03 (\u5408\u8a02\u672c) no.92-01, no. 92-04(\u5408\u8a02\u672c) ICG \u4e2d\u7684\u8ad6\u65e8\u89d2\u8272\u8207 A Conceptual 120 _____ _____ Structure for Parsing Mandarin --Its Frame and General Applications--US$ 9 US$ 19 US$15 _____ _____ 3. no.93-01 \u65b0\u805e\u8a9e\u6599\u5eab\u5b57\u983b\u7d71\u8a08\u8868 120 _____ _____ no.92-02 V-N \u8907\u5408\u540d\u8a5e\u8a0e\u8ad6\u7bc7 & 92-03 V-R \u8907\u5408\u52d5\u8a5e\u8a0e\u8ad6\u7bc7 12 21 17 _____ 4. no.93-02 \u65b0\u805e\u8a9e\u6599\u5eab\u8a5e\u983b\u7d71\u8a08\u8868 360 _____ _____ _____ Z Zeng, Yi-Ching 3. no.93-01 \u65b0\u805e\u8a9e\u6599\u5eab\u5b57\u983b\u7d71\u8a08\u8868 1. 2. 8 13 11 _____ 5. no.93-03 \u65b0\u805e\u5e38\u7528\u52d5\u8a5e\u8a5e\u983b\u8207\u5206\u985e 180 _____ _____ _____ 4. no.93-02 \u65b0\u805e\u8a9e\u6599\u5eab\u8a5e\u983b\u7d71\u8a08\u8868 18 30 24 _____ _____ 6. no.93-05 \u4e2d\u6587\u8a5e\u985e\u5206\u6790 185 _____ _____ Tsun Ku, Shih-Hung Wu, Liang-Pu Chen, and 5. no.93-03 \u65b0\u805e\u5e38\u7528\u52d5\u8a5e\u8a5e\u983b\u8207\u5206\u985e 10 15 13 _____ _____ 7. no.93-06 \u73fe\u4ee3\u6f22\u8a9e\u4e2d\u7684\u6cd5\u76f8\u8a5e 40 _____ _____ Gwo-Dong Chen. Modeling the Helpful M MAEKAWA, Kikuo see ASAHARA, Masayuki , 19(3): 1-24 Opinion Mining of Online Consumer Reviews 6. no.93-05 \u4e2d\u6587\u8a5e\u985e\u5206\u6790 10 15 13 _____ _____ 8. no.94-01 \u4e2d\u6587\u66f8\u9762\u8a9e\u983b\u7387\u8a5e\u5178(\u65b0\u805e\u8a9e\u6599\u8a5e\u983b\u7d71\u8a08) 380 _____ _____ as a Classification Problem; 19(2): 17-32 SUBJECT INDEX 7. no.93-06 \u73fe\u4ee3\u6f22\u8a9e\u4e2d\u7684\u6cd5\u76f8\u8a5e 5 10 8 _____ _____ 8. no.94-01 \u4e2d\u6587\u66f8\u9762\u8a9e\u983b\u7387\u8a5e\u5178(\u65b0\u805e\u8a9e\u6599\u8a5e\u983b\u7d71\u8a08) 18 30 24 _____ 9. no.94-02 \u53e4\u6f22\u8a9e\u5b57\u983b\u8868 180 _____ _____ _____ 9. no.94-02 \u53e4\u6f22\u8a9e\u5b57\u983b\u8868 11 16 14 _____ _____ 10. no.95-01 \u6ce8\u97f3\u6aa2\u7d22\u73fe\u4ee3\u6f22\u8a9e\u5b57\u983b\u8868 75 _____ _____ S Su, Chao-yu Chiu-yu Tseng and Jyh-Shing Roger Jang. Some Prosodic Characteristics of Taiwan English Accent; 19(4): 61-76 T Tran, Phuoc and Dien Dinh. A Novel Approach for Handling Unknown Word Problem in 10. no.95-01 \u6ce8\u97f3\u6aa2\u7d22\u73fe\u4ee3\u6f22\u8a9e\u5b57\u983b\u8868 8 13 10 _____ _____ 11. no.95-02/98-04 \u4e2d\u592e\u7814\u7a76\u9662\u5e73\u8861\u8a9e\u6599\u5eab\u7684\u5167\u5bb9\u8207\u8aaa\u660e 75 _____ _____ A Academic English Writing Automatic Move Analysis of Research Articles 11. no.95-02/98-04 \u4e2d\u592e\u7814\u7a76\u9662\u5e73\u8861\u8a9e\u6599\u5eab\u7684\u5167\u5bb9\u8207\u8aaa\u660e 3 8 6 _____ 12. no.95-03 \u8a0a\u606f\u70ba\u672c\u7684\u683c\u4f4d\u8a9e\u6cd5\u8207\u5176\u5256\u6790\u65b9\u6cd5 75 _____ _____ _____ 12. no.95-03 \u8a0a\u606f\u70ba\u672c\u7684\u683c\u4f4d\u8a9e\u6cd5\u8207\u5176\u5256\u6790\u65b9\u6cd5 3 8 6 _____ 13. no.96-01 \u300c\u641c\u300d\u6587\u89e3\u5b57-\u4e2d\u6587\u8a5e\u754c\u7814\u7a76\u8207\u8cc7\u8a0a\u7528\u5206\u8a5e\u6a19\u6e96 110 _____ _____ _____ for Assisting Writing; Huang, G.-C., 19(4): 13. no.96-01 \u300c\u641c\u300d\u6587\u89e3\u5b57-\u4e2d\u6587\u8a5e\u754c\u7814\u7a76\u8207\u8cc7\u8a0a\u7528\u5206\u8a5e\u6a19\u6e96 8 13 11 _____ _____ 14. no.97-01 \u53e4\u6f22\u8a9e\u8a5e\u983b\u8868 (\u7532) 400 _____ _____ 29-46 14. no.97-01 \u53e4\u6f22\u8a9e\u8a5e\u983b\u8868 (\u7532) 19 31 25 _____ _____ 15. no.97-02 \u8ad6\u8a9e\u8a5e\u983b\u8868 90 _____ _____ Arabic NLP Linking Databases using Matched Arabic Names; El-Shishtawy, T., 19(1): 33-52 15. no.97-02 \u8ad6\u8a9e\u8a5e\u983b\u8868 9 14 12 _____ _____ 16. no.98-01 \u8a5e\u983b\u8a5e\u5178 18 30 26 _____ 16 no.98-01 \u8a5e\u983b\u8a5e\u5178 395 _____ _____ _____ 17. no.98-02 Accumulated Word Frequency in CKIP Corpus 15 25 21 _____ 17. no.98-02 Accumulated Word Frequency in CKIP Corpus 340 _____ _____ _____ Huang, Guan-Cheng and Shu-Kai Hsieh. Back to the Basic: Exploring Jian-Cheng Wu, Hsiang-Ling Hsu, Tzu-Hsi Yen, and Jason S. Chang. Automatic Move Analysis of Research Articles for Assisting Writing; 19(4): 29-46 Chinese-Vietnamese Machine Translation; B 18. no.98-03 \u81ea\u7136\u8a9e\u8a00\u8655\u7406\u53ca\u8a08\u7b97\u8a9e\u8a00\u5b78\u76f8\u95dc\u8853\u8a9e\u4e2d\u82f1\u5c0d\u8b6f\u8868 4 9 7 _____ _____ 18. no.98-03 \u81ea\u7136\u8a9e\u8a00\u8655\u7406\u53ca\u8a08\u7b97\u8a9e\u8a00\u5b78\u76f8\u95dc\u8853\u8a9e\u4e2d\u82f1\u5c0d\u8b6f\u8868 90 _____ _____ 19(1): 1-10 Tsai, Richard Tzong-Han see Dai, Hong-Jie, 19(1): 11-32 see Wang, Yu-Chun, 19(3): 25-38 Bigram Model 19. no.02-01 \u73fe\u4ee3\u6f22\u8a9e\u53e3\u8a9e\u5c0d\u8a71\u8a9e\u6599\u5eab\u6a19\u8a3b\u7cfb\u7d71\u8aaa\u660e 8 13 11 _____ _____ 19. no.02-01 \u73fe\u4ee3\u6f22\u8a9e\u53e3\u8a9e\u5c0d\u8a71\u8a9e\u6599\u5eab\u6a19\u8a3b\u7cfb\u7d71\u8aaa\u660e 75 _____ _____ On the Use of Speech Recognition Techniques to Identify Bird Species; Tsai, W.-H., 19(1): 53-68 20. Computational Linguistics & Chinese Languages Processing (One year) (Back issues of IJCLCLP: US$ 20 per copy) ---100 100 _____ 20 \u8ad6\u6587\u96c6 COLING 2002 \u7d19\u672c 100 _____ _____ _____ 21. Readings in Chinese Language Processing 25 25 21 _____ _____ 21. \u8ad6\u6587\u96c6 COLING 2002 \u5149\u789f\u7247 300 _____ _____
Huang, Hen-Hsen Kai-Chun Chang, and Hsin-Hsi Chen. Modeling Human Inference Process for Textual Entailment Recognition; 19(3): 39-54 Huang, Shu-Ling Yu-Ming Hsieh, Su-Chu Lin, and Keh-Jiann Chen. Resolving the Representational Problems of Polarity and Interaction between Process and State Verbs; 19(2): 33-52 Huang, Ting-Hao (Kenneth) Social Metaphor Detection via Topical Analysis; 19(2): 1-16 I IMADA, Mizuho see ASAHARA, Masayuki , 19(3): 1-24 J Jang, Jyh-Shing Roger Bird Species Identification 300 _____ _____ On the Use of Speech Recognition Techniques to TOTAL _____ _____ 300 _____ _____ Identify Bird Species; Tsai, W.-H., 19(1): 53-68 10% member discount: ___________Total Due:__________ and Yu-Zhi Xue. On the Use of Speech Tsai, Wei-Ho Recognition Techniques to Identify Bird Species; 19(1): 55-68 Tseng, Chiu-yu see Su, Chao-yu, 19(4): 61-76 W Wang, Yu-Chun Karol Chia-Tien Chang, Richard Tzong-Han Tsai, and Jieh Hsiang. Transliteration Extraction from Classical Chinese Buddhist Literature Using Conditional Random Fields with Language Models; 19(3): 25-38 Wu, Jian-Cheng see Huang, Guan-Cheng, 19(4): 29-46 Wu, Shih-Hung 22. \u8ad6\u6587\u96c6 COLING 2002 Workshop \u5149\u789f\u7247 23. \u8ad6\u6587\u96c6 ISCSLP 2002 \u5149\u789f\u7247 \u4ea4\u8ac7\u7cfb\u7d71\u66a8\u8a9e\u5883\u5206\u6790\u7814\u8a0e\u6703\u8b1b\u7fa9 24. (\u4e2d\u83ef\u6c11\u570b\u8a08\u7b97\u8a9e\u8a00\u5b78\u5b78\u67031997\u7b2c\u56db\u5b63\u5b78\u8853\u6d3b\u52d5) 130 _____ _____ Buddhist Literation Transliteration Extraction from Classical Chinese Buddhist Literature Using Conditional Random Fields with Language Models; Wang, Y.-C., 19(3): 25-38 C Cause-result Relativity between Verbs Resolving the Representational Problems of \u2027 OVERSEAS USE ONLY \u4e2d\u6587\u8a08\u7b97\u8a9e\u8a00\u5b78\u671f\u520a (\u4e00\u5e74\u56db\u671f) \u5e74\u4efd\uff1a______ 25. (\u904e\u671f\u671f\u520a\u6bcf\u672c\u552e\u50f9500\u5143) ---2,500 _____ _____ \u2027 PAYMENT\uff1a \u25a1 Credit Card ( Preferred ) 26. Readings of Chinese Language Processing 675 _____ _____ 27. \u5256\u6790\u7b56\u7565\u8207\u6a5f\u5668\u7ffb\u8b6f 1990 150 _____ _____ \u5408 \u8a08 _____ _____ \u203b \u6b64\u50f9\u683c\u8868\u50c5\u9650\u570b\u5167 (\u53f0\u7063\u5730\u5340) \u4f7f\u7528 \u25a1 Name (please print): Signature: \u5283\u64a5\u5e33\u6236\uff1a\u4e2d\u83ef\u6c11\u570b\u8a08\u7b97\u8a9e\u8a00\u5b78\u5b78\u6703 \u5283\u64a5\u5e33\u865f\uff1a19166251 Polarity and Interaction between Process and State Verbs; Huang, S.-L., 19(2): 33-52 Fax: \uf997\u7d61\u96fb\u8a71\uff1a(02) 2788-3799 \u8f491502 E-mail: \uf997\u7d61\u4eba\uff1a \u9ec3\u742a \u5c0f\u59d0\u3001\u4f55\u5a49\u5982 \u5c0f\u59d0 E-mail:aclclp@hp.iis.sinica.edu.tw CEFR Salient Linguistic Features of Chinese Learners with Different L1s: A Corpus-based Study; \u8a02\u8cfc\u8005\uff1a \u6536\u64da\u62ac\u982d\uff1a Address\uff1a \u5730 \u5740\uff1a see Zeng, Yi-Ching, 19(2): 17-32 Chang, L.-p., 19(2): 53-72 \u96fb \u8a71\uff1a E-mail:
see Su, Chao-yu, 19(4): 61-76
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