waashk/dblp huggingface dataset:
a practically unsupervised learning method to identify single-snippet answers to definition questions on the web. we present a practically unsupervised learning method to produce single-snippet answers to definition questions in question answering systems that supplement web search engines. the method exploits on-line encyclopedias and dictionaries to generate automatically an arbitrarily large number of positive and negative definition examples, which are then used to train an svm to separate the two classes. we show experimentally that the proposed method is viable, that it outperforms the alternative of training the system on questions and news articles from trec, and that it helps the search engine handle definition questions significantly better.
robust named entity extraction from large spoken archives. traditional approaches to information extraction (ie) from speech input simply consist in applying text based methods to the output of an automatic speech recognition (asr) system. if it gives satisfaction with low word error rate (wer) transcripts, we believe that a tighter integration of the ie and asr modules can increase the ie performance in more difficult conditions. more specifically this paper focuses on the robust extraction of named entities from speech input where a temporal mismatch between training and test corpora occurs. we describe a named entity recognition (ner) system, developed within the french rich broadcast news transcription program ester, which is specifically optimized to process asr transcripts and can be integrated into the search process of the asr modules. finally we show how some metadata information can be collected in order to adapt ner and asr models to new conditions and how they can be used in a task of named entity indexation of spoken archives.
neuralign: combining word alignments using neural networks. this paper presents a novel approach to combining different word alignments. we view word alignment as a pattern classification problem, where alignment combination is treated as a classifier ensemble, and alignment links are adorned with linguistic features. a neural network model is used to learn word alignments from the individual alignment systems. we show that our alignment combination approach yields a significant 20--34% relative error reduction over the best-known alignment combination technique on english-spanish and english-chinese data.
tell me what you do and i'll tell you what you are: learning occupation-related activities for biographies. biography creation requires the identification of important events in the life of the individual in question. while there are events such as birth and death that apply to everyone, most of the other activities tend to be occupation-specific. hence, occupation gives important clues as to which activities should be included in the biography. we present techniques for automatically identifying which important events apply to the general population, which ones are occupation-specific, and which ones are person-specific. we use the extracted information as features for a multi-class svm classifier, which is then used to automatically identify the occupation of a previously unseen individual. we present experiments involving 189 individuals from ten occupations, and we show that our approach accurately identifies general and occupation-specific activities and assigns unseen individuals to the correct occupations. finally, we present evidence that our technique can lead to efficient and effective biography generation relying only on statistical techniques.
alignment link projection using transformation-based learning. we present a new word-alignment approach that learns errors made by existing word alignment systems and corrects them. by adapting transformation-based learning to the problem of word alignment, we project new alignment links from already existing links, using features such as pos tags. we show that our alignment link projection approach yields a significantly lower alignment error rate than that of the best performing alignment system (22.6% relative reduction on english-spanish data and 23.2% relative reduction on english-chinese data).
a cost-benefit analysis of hybrid phone-manner representations for asr. in the past decade, several researchers have started reinvestigating the use of sub-phonetic models for lexical representations within automatic speech recognition systems. lest history repeat itself, it may be instructive to mine the further past for models of lexical representations in the lexical access literature. in this work, we re-evaluate the model of briscoe (1989), in which a hybrid strategy of lexical representation between phones and manner classes is promoted. while many of briscoe's assumptions do not match up with current asr processing models, we show that his conclusions are essentially correct, and that reconsidering this structure for asr lexica is an appropriate avenue for future asr research.
predicting sentences using n-gram language models. we explore the benefit that users in several application areas can experience from a "tab-complete" editing assistance function. we develop an evaluation metric and adapt n-gram language models to the problem of predicting the subsequent words, given an initial text fragment. using an instance-based method as baseline, we empirically study the predictability of call-center emails, personal emails, weather reports, and cooking recipes.
disambiguating toponyms in news. this research is aimed at the problem of disambiguating toponyms (place names) in terms of a classification derived by merging information from two publicly available gazetteers. to establish the difficulty of the problem, we measured the degree of ambiguity, with respect to a gazetteer, for toponyms in news. we found that 67.82% of the toponyms found in a corpus that were ambiguous in a gazetteer lacked a local discriminator in the text. given the scarcity of human-annotated data, our method used unsupervised machine learning to develop disambiguation rules. toponyms were automatically tagged with information about them found in a gazetteer. a toponym that was ambiguous in the gazetteer was automatically disambiguated based on preference heuristics. this automatically tagged data was used to train a machine learner, which disambiguated toponyms in a human-annotated news corpus at 78.5% accuracy.
the vocal joystick: a voice-based human-computer interface for individuals with motor impairments. we present a novel voice-based human-computer interface designed to enable individuals with motor impairments to use vocal parameters for continuous control tasks. since discrete spoken commands are ill-suited to such tasks, our interface exploits a large set of continuous acoustic-phonetic parameters like pitch, loudness, vowel quality, etc. their selection is optimized with respect to automatic recognizability, communication bandwidth, learnability, suitability, and ease of use. parameters are extracted in real time, transformed via adaptation and acceleration, and converted into continuous control signals. this paper describes the basic engine, prototype applications (in particular, voice-based web browsing and a controlled trajectory-following task), and initial user studies confirming the feasibility of this technology.
improving statistical mt through morphological analysis. in statistical machine translation, estimating word-to-word alignment probabilities for the translation model can be difficult due to the problem of sparse data: most words in a given corpus occur at most a handful of times. with a highly inflected language such as czech, this problem can be particularly severe. in addition, much of the morphological variation seen in czech words is not reflected in either the morphology or syntax of a language like english. in this work, we show that using morphological analysis to modify the czech input can improve a czech-english machine translation system. we investigate several different methods of incorporating morphological information, and show that a system that combines these methods yields the best results. our final system achieves a bleu score of .333, as compared to .270 for the baseline word-to-word system.
robust textual inference via graph matching. we present a system for deciding whether a given sentence can be inferred from text. each sentence is represented as a directed graph (extracted from a dependency parser) in which the nodes represent words or phrases, and the links represent syntactic and semantic relationships. we develop a learned graph matching approach to approximate entailment using the amount of the sentence's semantic content which is contained in the text. we present results on the recognizing textual entailment dataset (dagan et al., 2005), and show that our approach outperforms bag-of-words and tf-idf models. in addition, we explore common sources of errors in our approach and how to remedy them.
recognising textual entailment with logical inference. we use logical inference techniques for recognising textual entailment. as the performance of theorem proving turns out to be highly dependent on not readily available background knowledge, we incorporate model building, a technique borrowed from automated reasoning, and show that it is a useful robust method to approximate entailment. finally, we use machine learning to combine these deep semantic analysis techniques with simple shallow word overlap; the resulting hybrid model achieves high accuracy on the rte testset, given the state of the art. our results also show that the different techniques that we employ perform very differently on some of the subsets of the rte corpus and as a result, it is useful to use the nature of the dataset as a feature.
comparing and combining finite-state and context-free parsers. in this paper, we look at comparing high-accuracy context-free parsers with high-accuracy finite-state (shallow) parsers on several shallow parsing tasks. we show that previously reported comparisons greatly under-estimated the performance of context-free parsers for these tasks. we also demonstrate that context-free parsers can train effectively on relatively little training data, and are more robust to domain shift for shallow parsing tasks than has been previously reported. finally, we establish that combining the output of context-free and finite-state parsers gives much higher results than the previous-best published results, on several common tasks. while the efficiency benefit of finite-state models is inarguable, the results presented here show that the corresponding cost in accuracy is higher than previously thought.
automatic question generation for vocabulary assessment. in the reap system, users are automatically provided with texts to read targeted to their individual reading levels. to find appropriate texts, the user's vocabulary knowledge must be assessed. we describe an approach to automatically generating questions for vocabulary assessment. traditionally, these assessments have been hand-written. using data from wordnet, we generate 6 types of vocabulary questions. they can have several forms, including wordbank and multiple-choice. we present experimental results that suggest that these automatically-generated questions give a measure of vocabulary skill that correlates well with subject performance on independently developed human-written questions. in addition, strong correlations with standardized vocabulary tests point to the validity of our approach to automatic assessment of word knowledge.
cluster-specific named entity transliteration. existing named entity (ne) transliteration approaches often exploit a general model to transliterate nes, regardless of their origins. as a result, both a chinese name and a french name (assuming it is already translated into chinese) will be translated into english using the same model, which often leads to unsatisfactory performance. in this paper we propose a cluster-specific ne transliteration framework. we group name origins into a smaller number of clusters, then train transliteration and language models for each cluster under a statistical machine translation framework. given a source ne, we first select appropriate models by classifying it into the most likely cluster, then we transliterate this ne with the corresponding models. we also propose a phrase-based name transliteration model, which effectively combines context information for transliteration. our experiments showed substantial improvement on the transliteration accuracy over a state-of-the-art baseline system, significantly reducing the transliteration character error rate from 50.29% to 12.84%.
semantic similarity for detecting recognition errors in automatic speech transcripts. browsing through large volumes of spoken audio is known to be a challenging task for end users. one way to alleviate this problem is to allow users to gist a spoken audio document by glancing over a transcript generated through automatic speech recognition. unfortunately, such transcripts typically contain many recognition errors which are highly distracting and make gisting more difficult. in this paper we present an approach that detects recognition errors by identifying words which are semantic outliers with respect to other words in the transcript. we describe several variants of this approach. we investigate a wide range of evaluation measures and we show that we can significantly reduce the number of errors in content words, with the trade-off of losing some good content words.
knowitnow: fast, scalable information extraction from the web. numerous nlp applications rely on search-engine queries, both to extract information from and to compute statistics over the web corpus. but search engines often limit the number of available queries. as a result, query-intensive nlp applications such as information extraction (ie) distribute their query load over several days, making ie a slow, offline process.this paper introduces a novel architecture for ie that obviates queries to commercial search engines. the architecture is embodied in a system called knowitnow that performs high-precision ie in minutes instead of days. we compare knowitnow experimentally with the previously-published knowitall system, and quantify the tradeoff between recall and speed. knowitnow's extraction rate is two to three orders of magnitude higher than knowitall's.
using names and topics for new event detection. new event detection (ned) involves monitoring chronologically-ordered news streams to automatically detect the stories that report on new events. we compare two stories by finding three cosine similarities based on names, topics and the full text. these additional comparisons suggest treating the ned problem as a binary classification problem with the comparison scores serving as features. the classifier models we learned show statistically significant improvement over the baseline vector space model system on all the collections we tested, including the latest tdt5 collection.the presence of automatic speech recognizer (asr) output of broadcast news in news streams can reduce performance and render our named entity recognition based approaches ineffective. we provide a solution to this problem achieving statistically significant improvements.
a salience driven approach to robust input interpretation in multimodal conversational systems. to improve the robustness in multimodal input interpretation, this paper presents a new salience driven approach. this approach is based on the observation that, during multimodal conversation, information from deictic gestures (e.g., point or circle) on a graphical display can signal a part of the physical world (i.e., representation of the domain and task) of the application which is salient during the communication. this salient part of the physical world will prime what users tend to communicate in speech and in turn can be used to constrain hypotheses for spoken language understanding, thus improving overall input interpretation. our experimental results have indicated the potential of this approach in reducing word error rate and improving concept identification in multimodal conversation.
multi-lingual coreference resolution with syntactic features. in this paper, we study the impact of a group of features extracted automatically from machine-generated parse trees on coreference resolution. one focus is on designing syntactic features using the binding theory as the guideline to improve pronoun resolution, although linguistic phenomenon such as apposition is also modeled. these features are applied to the arabic, chinese and english coreference resolution systems and their effectiveness is evaluated on data from the automatic content extraction (ace) task. the syntactic features improve the arabic and english systems significantly, but play a limited role in the chinese one. detailed analyses are done to understand the syntactic features' impact on the three coreference systems.
the hiero machine translation system: extensions, evaluation, and analysis. hierarchical organization is a well known property of language, and yet the notion of hierarchical structure has been largely absent from the best performing machine translation systems in recent community-wide evaluations. in this paper, we discuss a new hierarchical phrase-based statistical machine translation system (chiang, 2005), presenting recent extensions to the original proposal, new evaluation results in a community-wide evaluation, and a novel technique for fine-grained comparative analysis of mt systems.
a robust combination strategy for semantic role labeling. this paper focuses on semantic role labeling using automatically-generated syntactic information. a simple and robust strategy for system combination is presented, which allows to partially recover from input parsing errors and to significantly boost results of individual systems. this combination scheme is also very flexible since the individual systems are not required to provide any information other than their solution. extensive experimental evaluation in the conll-2005 shared task framework supports our previous claims. the proposed architecture outperforms the best results reported in that evaluation exercise.
identifying sources of opinions with conditional random fields and extraction patterns. recent systems have been developed for sentiment classification, opinion recognition, and opinion analysis (e.g., detecting polarity and strength). we pursue another aspect of opinion analysis: identifying the sources of opinions, emotions, and sentiments. we view this problem as an information extraction task and adopt a hybrid approach that combines conditional random fields (lafferty et al., 2001) and a variation of autoslog (riloff, 1996a). while crfs model source identification as a sequence tagging task, autoslog learns extraction patterns. our results show that the combination of these two methods performs better than either one alone. the resulting system identifies opinion sources with 79.3% precision and 59.5% recall using a head noun matching measure, and 81.2% precision and 60.6% recall using an overlap measure.
a generalized framework for revealing analogous themes across related topics. this work addresses the task of identifying thematic correspondences across sub-corpora focused on different topics. we introduce an unsupervised algorithmic framework based on distributional data clustering, which generalizes previous initial works on this task. the empirical results reveal interesting commonalities of different religions. we evaluate the results through measuring the overlap of our clusters with clusters compiled manually by experts. the tested variants of our framework are shown to outperform alternative methods applicable to the task.
a methodology for extrinsically evaluating information extraction performance. this paper reports a preliminary study addressing two challenges in measuring the effectiveness of information extraction (ie) technology:&bull; developing a methodology for extrinsic evaluation of ie; and,&bull; estimating the impact of improving ie technology on the ability to perform an application task.the methodology described can be employed for further controlled experiments regarding information extraction.
hmm word and phrase alignment for statistical machine translation. hmm-based models are developed for the alignment of words and phrases in bitext. the models are formulated so that alignment and parameter estimation can be performed efficiently. we find that chinese-english word alignment performance is comparable to that of ibm model-4 even over large training bitexts. phrase pairs extracted from word alignments generated under the model can also be used for phrase-based translation, and in chinese to english and arabic to english translation, performance is comparable to systems based on model-4 alignments. direct phrase pair induction under the model is described and shown to improve translation performance.
securing web access with dce. internet tools, especially web browsers and servers, are being widely used for information access. however, these tools have some limitations in terms of the security available for those information accesses and of the robustness and availability of the infrastructure used to provide that security. this paper describes work done to utilize the security services and infrastructure of the open software foundation (osf) distributed computing environment (dce) to secure web accesses. this work was done as part of an advanced technology offering (ato) by the osf research institute jointly with gradient technologies inc. and other ato sponsors. a practical implementation has been completed. these combined technologies allow users to securely access both web documents and application servers from a variety of desktop systems using standard, off-the-shelf web browsers. the acm portal is published by the association for computing machinery. copyright © 2010 acm, inc. terms of usage privacy policy code of ethics contact us useful downloads: adobe acrobat quicktime windows media player real player
reducing the cost of security in link-state routing. security in link-state routing protocols is a feature that is both desirable and costly. this paper examines the cost of security and presents two techniques for efficient and secure processing of link state updates. the first technique is geared towards a relatively stable internetwork environment while the second is designed with a more volatile environment in mind.
improved proxy re-encryption schemes with applications to secure distributed storage. in 1998, blaze, bleumer, and strauss (bbs) proposed an application called atomic proxy re-encryption, in which a semitrusted proxy converts a ciphertext for alice into a ciphertext for bob without seeing the underlying plaintext. we predict that fast and secure re-encryption will become increasingly popular as a method for managing encrypted file systems. although efficiently computable, the wide-spread adoption of bbs re-encryption has been hindered by considerable security risks. following recent work of dodis and ivan, we present new re-encryption schemes that realize a stronger notion of security and demonstrate the usefulness of proxy re-encryption as a method of adding access control to a secure file system. performance measurements of our experimental file system demonstrate that proxy re-encryption can work effectively in practice.
probable plaintext cryptanalysis of the ip security protocols. the internet engineering task force (ietf) is in the process of adopting standards for ip-layer encryption and authentication (ipsec). we describe how "probable plaintext" can be used to aid in cryptanalytic attacks, and analyze the protocol to show how much probable plaintext is available. we also show how traffic analysis is a powerful aid to the cryptanalyst. we conclude by outlining some likely changes to the underlying protocols that may strengthen them against these attacks.