waashk/dblp huggingface dataset:
enhancing multilingual latent semantic analysis with term alignment information. latent semantic analysis (lsa) is based on the singular value decomposition (svd) of a term-by-document matrix for identifying relationships among terms and documents from cooccurrence patterns. among the multiple ways of computing the svd of a rectangular matrix x, one approach is to compute the eigenvalue decomposition (evd) of a square 2 x 2 composite matrix consisting of four blocks with x and xt in the off-diagonal blocks and zero matrices in the diagonal blocks. we point out that significant value can be added to lsa by filling in some of the values in the diagonal blocks (corresponding to explicit term-to-term or document-to-document associations) and computing a term-by-concept matrix from the evd. for the case of multilingual lsa, we incorporate information on cross-language term alignments of the same sort used in statistical machine translation (smt). since all elements of the proposed evd-based approach can rely entirely on lexical statistics, hardly any price is paid for the improved empirical results. in particular, the approach, like lsa or smt, can still be generalized to virtually any language(s); computation of the evd takes similar resources to that of the svd since all the blocks are sparse; and the results of evd are just as economical as those of svd.
a probabilistic model for measuring grammaticality and similarity of automatically generated paraphrases of predicate phrases. the most critical issue in generating and recognizing paraphrases is development of wide-coverage paraphrase knowledge. previous work on paraphrase acquisition has collected lexicalized pairs of expressions; however, the results do not ensure full coverage of the various paraphrase phenomena. this paper focuses on productive paraphrases realized by general transformation patterns, and addresses the issues in generating instances of phrasal paraphrases with those patterns. our probabilistic model computes how two phrases are likely to be correct paraphrases. the model consists of two components: (i) a structured n-gram language model that ensures grammaticality and (ii) a distributional similarity measure for estimating semantic equivalence and substitutability.
translating queries into snippets for improved query expansion. user logs of search engines have recently been applied successfully to improve various aspects of web search quality. in this paper, we will apply pairs of user queries and snippets of clicked results to train a machine translation model to bridge the "lexical gap" between query and document space. we show that the combination of a query-to-snippet translation model with a large n-gram language model trained on queries achieves improved contextual query expansion compared to a system based on term correlations.
coordination disambiguation without any similarities. the use of similarities has been one of the main approaches to resolve the ambiguities of coordinate structures. in this paper, we present an alternative method for coordination disambiguation, which does not use similarities. our hypothesis is that coordinate structures are supported by surrounding dependency relations, and that such dependency relations rather yield similarity between conjuncts, which humans feel. based on this hypothesis, we built a japanese fully-lexicalized generative parser that includes coordination disambiguation. experimental results on web sentences indicated the effectiveness of our approach, and endorsed our hypothesis.
switching to real-time tasks in multi-tasking dialogue. in this paper we describe an empirical study of human-human multi-tasking dialogues (mtd), where people perform multiple verbal tasks overlapped in time. we examined how conversants switch from the ongoing task to a real-time task. we found that 1) conversants use discourse markers and prosodic cues to signal task switching, similar to how they signal topic shifts in single-tasking speech; 2) conversants strive to switch tasks at a less disruptive place; and 3) where they cannot, they exert additional effort (even higher pitch) to signal the task switching. our machine learning experiment also shows that task switching can be reliably recognized using discourse context and normalized pitch. these findings will provide guidelines for building future speech interfaces to support multi-tasking dialogue.
domain adaptation for statistical machine translation with domain dictionary and monolingual corpora. statistical machine translation systems are usually trained on large amounts of bilingual text and monolingual text. in this paper, we propose a method to perform domain adaptation for statistical machine translation, where in-domain bilingual corpora do not exist. this method first uses out-of-domain corpora to train a baseline system and then uses in-domain translation dictionaries and in-domain monolingual corpora to improve the in-domain performance. we propose an algorithm to combine these different resources in a unified framework. experimental results indicate that our method achieves absolute improvements of 8.16 and 3.36 bleu scores on chinese to english translation and english to french translation respectively, as compared with the baselines using only out-of-domain corpora.
prediction of maximal projection for semantic role labeling. in semantic role labeling (srl), arguments are usually limited in a syntax subtree. it is reasonable to label arguments locally in such a sub-tree rather than a whole tree. to identify active region of arguments, this paper models maximal projection (mp), which is a concept in d-structure from the projection principle of the principle and parameters theory. this paper makes a new definition of mp in s-structure and proposes two methods to predict it: the anchor group approach and the single anchor approach. the anchor group approach achieves an accuracy of 87.75% and the single anchor approach achieves 83.63%. experimental results also indicate that the prediction of mp improves semantic role labeling.
parametric: an automatic evaluation metric for paraphrasing. we present parametric, an automatic evaluation metric for data-driven approaches to paraphrasing. parametric provides an objective measure of quality using a collection of multiple translations whose paraphrases have been manually annotated. parametric calculates precision and recall scores by comparing the paraphrases discovered by automatic paraphrasing techniques against gold standard alignments of words and phrases within equivalent sentences. we report scores for several established paraphrasing techniques.
word lattice reranking for chinese word segmentation and part-of-speech tagging. in this paper, we describe a new reranking strategy named word lattice reranking, for the task of joint chinese word segmentation and part-of-speech (pos) tagging. as a derivation of the forest reranking for parsing (huang, 2008), this strategy reranks on the pruned word lattice, which potentially contains much more candidates while using less storage, compared with the traditional n-best list reranking. with a perceptron classifier trained with local features as the baseline, word lattice reranking performs reranking with non-local features that can't be easily incorporated into the perceptron baseline. experimental results show that, this strategy achieves improvement on both segmentation and pos tagging, above the perceptron baseline and the n-best list reranking.
a fully-lexicalized probabilistic model for japanese zero anaphora resolution. this paper presents a probabilistic model for japanese zero anaphora resolution. first, this model recognizes discourse entities and links all mentions to them. zero pronouns are then detected by case structure analysis based on automatically constructed case frames. their appropriate antecedents are selected from the entities with high salience scores, based on the case frames and several preferences on the relation between a zero pronoun and an antecedent. case structure and zero anaphora relation are simultaneously determined based on probabilistic evaluation metrics.
a discriminative alignment model for abbreviation recognition. this paper presents a discriminative alignment model for extracting abbreviations and their full forms appearing in actual text. the task of abbreviation recognition is formalized as a sequential alignment problem, which finds the optimal alignment (origins of abbreviation letters) between two strings (abbreviation and full form). we design a large amount of finegrained features that directly express the events where letters produce or do not produce abbreviations. we obtain the optimal combination of features on an aligned abbreviation corpus by using the maximum entropy framework. the experimental results show the usefulness of the alignment model and corpus for improving abbreviation recognition.
semantic role assignment for event nominalisations by leveraging verbal data. this paper presents a novel approach to the task of semantic role labelling for event nominalisations, which make up a considerable fraction of predicates in running text, but are underrepresented in terms of training data and difficult to model. we propose to address this situation by data expansion. we construct a model for nominal role labelling solely from verbal training data. the best quality results from salvaging grammatical features where applicable, and generalising over lexical heads otherwise.
representations for category disambiguation. as it serves as a basis for pos tagging, category induction, and human category acquisition, we investigate the information needed to disambiguate a word in a local context, when using corpus categories. specifically, we increase the recall of an error detection method by abstracting the word to be disambiguated to a representation containing information about some of its inherent properties, namely the set of categories it can potentially have. this work thus provides insights into the relation of corpus categories to categories derived from local contexts.
extracting synchronous grammar rules from word-level alignments in linear time. we generalize uno and yagiura's algorithm for finding all common intervals of two permutations to the setting of two sequences with many-to-many alignment links across the two sides. we show how to maximally decompose a word-aligned sentence pair in linear time, which can be used to generate all possible phrase pairs or a synchronous context-free grammar (scfg) with the simplest rules possible. we also use the algorithm to precisely analyze the maximum scfg rule length needed to cover hand-aligned data from various language pairs.
knownet: building a large net of knowledge from the web. this paper presents a new fully automatic method for building highly dense and accurate knowledge bases from existing semantic resources. basically, the method uses a wide-coverage and accurate knowledge-based word sense disambiguation algorithm to assign the most appropriate senses to large sets of topically related words acquired from the web. knownet, the resulting knowledge-base which connects large sets of semantically-related concepts is a major step towards the autonomous acquisition of knowledge from raw corpora. in fact, knownet is several times larger than any available knowledge resource encoding relations between synsets, and the knowledge knownet contains outperform any other resource when is empirically evaluated in a common framework.
robust similarity measures for named entities matching. matching coreferent named entities without prior knowledge requires good similarity measures. soft-tfidf is a fine-grained measure which performs well in this task. we propose to enhance this kind of metrics, through a generic model in which measures may be mixed, and show experimentally the relevance of this approach.
what's the date? high accuracy interpretation of weekday names. in this paper we present a study on the interpretation of weekday names in texts. our algorithm for assigning a date to a weekday name achieves 95.91% accuracy on a test data set based on the ace 2005 training corpus, outperforming previously reported techniques run against this same data. we also provide the first detailed comparison of various approaches to the problem using this test data set, employing re-implementations of key techniques from the literature and a range of additional heuristic-based approaches.
retrieving bilingual verb-noun collocations by integrating cross-language category hierarchies. this paper presents a method of retrieving bilingual collocations of a verb and its objective noun from cross-lingual documents with similar contents. relevant documents are obtained by integrating cross-language hierarchies. the results showed a 15.1% improvement over the baseline non-hierarchy model, and a 6.0% improvement over use of relevant documents retrieved from a single hierarchy. moreover, we found that some of the retrieved collocations were domain-specific.
mining opinions in comparative sentences. this paper studies sentiment analysis from the user-generated content on the web. in particular, it focuses on mining opinions from comparative sentences, i.e., to determine which entities in a comparison are preferred by its author. a typical comparative sentence compares two or more entities. for example, the sentence, "the picture quality of camera x is better than that of camera y", compares two entities "camera x" and "camera y" with regard to their picture quality. clearly, "camera x" is the preferred entity. existing research has studied the problem of extracting some key elements in a comparative sentence. however, there is still no study of mining opinions from comparative sentences, i.e., identifying preferred entities of the author. this paper studies this problem, and proposes a technique to solve the problem. our experiments using comparative sentences from product reviews and forum posts show that the approach is effective.
extending a thesaurus with words from pan-chinese sources. in this paper, we work on extending a chinese thesaurus with words distinctly used in various chinese communities. the acquisition and classification of such region-specific lexical items is an important step toward the larger goal of constructing a pan-chinese lexical resource. in particular, we extend a previous study in three respects: (1) to improve automatic classification by removing duplicated words from the thesaurus, (2) to experiment with classifying words at the subclass level and semantic head level, and (3) to further investigate the possible effects of data heterogeneity between the region-specific words and words in the thesaurus on classification performance. automatic classification was based on the similarity between a target word and individual categories of words in the thesaurus, measured by the cosine function. experiments were done on 120 target words from four regions. the automatic classification results were evaluated against a gold standard obtained from human judgements. in general accuracy reached 80% or more with the top 10 (out of 80+) and top 100 (out of 1,300+) candidates considered at the subclass level and semantic head level respectively, provided that the appropriate data sources were used.
relational-realizational parsing. state-of-the-art statistical parsing models applied to free word-order languages tend to underperform compared to, e.g., parsing english. constituency-based models often fail to capture generalizations that cannot be stated in structural terms, and dependency-based models employ a 'single-head' assumption that often breaks in the face of multiple exponence. in this paper we suggest that the position of a constituent is a form manifestation of its grammatical function, one among various possible means of realization. we develop the relational-realizational approach to parsing in which we untangle the projection of grammatical functions and their means of realization to allow for phrase-structure variability and morphological-syntactic interaction. we empirically demonstrate the application of our approach to parsing modern hebrew, obtaining 7% error reduction from previously reported results.
japanese dependency parsing using a tournament model. in japanese dependency parsing, kudo's relative preference-based method (kudo and matsumoto, 2005) outperforms both deterministic and probabilistic cky-based parsing methods. in kudo's method, for each dependent word (or chunk) a log-linear model estimates relative preference of all other candidate words (or chunks) for being as its head. this cannot be considered in the deterministic parsing methods. we propose an algorithm based on a tournament model, in which the relative preferences are directly modeled by one-on-one games in a step-ladder tournament. in an evaluation experiment with kyoto text corpus version 4.0, the proposed method outperforms previous approaches, including the relative preference-based method.
semantic classification with distributional kernels. distributional measures of lexical similarity and kernel methods for classification are well-known tools in natural language processing. we bring these two methods together by introducing distributional kernels that compare co-occurrence probability distributions. we demonstrate the effectiveness of these kernels by presenting state-of-the-art results on datasets for three semantic classification: compound noun interpretation, identification of semantic relations between nominals and semantic classification of verbs. finally, we consider explanations for the impressive performance of distributional kernels and sketch some promising generalisations.
verification and implementation of language-based deception indicators in civil and criminal narratives. our goal is to use natural language processing to identify deceptive and non-deceptive passages in transcribed narratives. we begin by motivating an analysis of language-based deception that relies on specific linguistic indicators to discover deceptive statements. the indicator tags are assigned to a document using a mix of automated and manual methods. once the tags are assigned, an interpreter automatically discriminates between deceptive and truthful statements based on tag densities. the texts used in our study come entirely from "real world" sources---criminal statements, police interrogations and legal testimony. the corpus was hand-tagged for the truth value of all propositions that could be externally verified as true or false. classification and regression tree techniques suggest that the approach is feasible, with the model able to identify 74.9% of the t/f propositions correctly. implementation of an automatic tagger with a large subset of tags performed well on test data, producing an average score of 68.6% recall and 85.3% precision when compared to the performance of human taggers on the same subset.
a syntactic time-series model for parsing fluent and disfluent speech. this paper describes an incremental approach to parsing transcribed spontaneous speech containing disfluencies with a hierarchical hidden markov model (hhmm). this model makes use of the right-corner transform, which has been shown to increase non-incremental parsing accuracy on transcribed spontaneous speech (miller and schuler, 2008), using trees transformed in this manner to train the hhmm parser. not only do the representations used in this model align with structure in speech repairs, but as an hmm-like time-series model, it can be directly integrated into conventional speech recognition systems run on continuous streams of audio. a system implementing this model is evaluated on the standard task of parsing the switchboard corpus, and achieves an improvement over the standard baseline probabilistic cyk parser.
hybrid processing for grammar and style checking. this paper presents an implemented hybrid approach to grammar and style checking, combining an industrial pattern-based grammar and style checker with bidirectional, large-scale hpsg grammars for german and english. under this approach, deep processing is applied selectively based on the error hypotheses of a shallow system. we have conducted a comparative evaluation of the two components, supporting an integration scenario where the shallow system is best used for error detection, whereas the hpsg grammars add error correction for both grammar and controlled language style errors.
reading the markets: forecasting public opinion of political candidates by news analysis. media reporting shapes public opinion which can in turn influence events, particularly in political elections, in which candidates both respond to and shape public perception of their campaigns. we use computational linguistics to automatically predict the impact of news on public perception of political candidates. our system uses daily newspaper articles to predict shifts in public opinion as reflected in prediction markets. we discuss various types of features designed for this problem. the news system improves market prediction over baseline market systems.
measuring and predicting orthographic associations: modelling the similarity of japanese kanji. as human beings, our mental processes for recognising linguistic symbols generate perceptual neighbourhoods around such symbols where confusion errors occur. such neighbourhoods also provide us with conscious mental associations between symbols. this paper formalises orthographic models for similarity of japanese kanji, and provides a proof-of-concept dictionary extension leveraging the mental associations provided by orthographic proximity.
random restarts in minimum error rate training for statistical machine translation. och's (2003) minimum error rate training (mert) procedure is the most commonly used method for training feature weights in statistical machine translation (smt) models. the use of multiple randomized starting points in mert is a well-established practice, although there seems to be no published systematic study of its benefits. we compare several ways of performing random restarts with mert. we find that all of our random restart methods outperform mert without random restarts, and we develop some refinements of random restarts that are superior to the most common approach with regard to resulting model quality and training time.
unsupervised induction of labeled parse trees by clustering with syntactic features. we present an algorithm for unsupervised induction of labeled parse trees. the algorithm has three stages: bracketing, initial labeling, and label clustering. bracketing is done from raw text using an unsupervised incremental parser. initial labeling is done using a merging model that aims at minimizing the grammar description length. finally, labels are clustered to a desired number of labels using syntactic features extracted from the initially labeled trees. the algorithm obtains 59% labeled f-score on the wsj10 corpus, as compared to 35% in previous work, and substantial error reduction over a random baseline. we report results for english, german and chinese corpora, using two label mapping methods and two label set sizes.