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Arabic cross-document coreference detection

Arabic cross-document coreference detection

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Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and 4th International Joint Conference on Natural language processing of the AFNLP, ACL-IJCNLP 2009

作者： Sayeed, Asad Elsayed, Tamer Garera, Nikesh Alexander, David Xu, Tan Oard, Douglas W. Yarowsky, David Piatko, Christine Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD United States Dept. of Computer Science University of Maryland College Park MD United States BBN Technologies Cambridge MA United States College of Information Studies University of Maryland College Park MD United States UMIACS University of Maryland College Park MD United States Dept. of Computer Science Johns Hopkins University Baltimore MD United States

ISBN: (纸本)9781617382581

We describe a set of techniques for Arabic cross-document coreference resolution. We compare a baseline system of exact mention string-matching to ones that include local mention context information as well as information from an existing machine translation system. It turns out that the machine translation-based technique outperforms the baseline, but local entity context similarity does not. This helps to point the way for future crossdocument coreference work in languages with few existing resources for the task. © 2009 ACL and AFNLP.

关键词： Machine translation

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Retrieval experiments at Morpho Challenge 2008

Retrieval experiments at Morpho Challenge 2008

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2008 Working Notes for CLEF Workshop, CLEF 2008 - Co-located with the 12th European Conference on Digital Libraries, ECDL 2008

作者： McNamee, Paul JHU Human Language Technology Center of Excellence United States

Morpho Challenge 2008 hosted an extrinsic evaluation of morphological analysis that explored whether unsupervised morphology induction could benefit information retrieval. This paper presents results in alternative methods for word normalization using test sets from the Cross-language Evaluation Forum (CLEF) ad-hoc collections. Preliminary results for the Morpho Challenge 2008 evaluation are consistent with these data. We found that: (1) rule-based stemming is effective in less morphologically complicated languages;(2) alternative methods for stemming such as unsupervised learning of morphemes and least common n-gram stemming are helpful;and, (3) full character n-gram indexing is the most effective form of tokenization in more morphologically complex languages.

关键词： Computational linguistics

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Pairwise document similarity in large collections with MapReduce 46

Pairwise document similarity in large collections with MapRe...

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46th Annual Meeting of the Association for Computational Linguistics: human language Technologies, ACL 2008

作者： Elsayed, Tamer Lin, Jimmy Oard, Douglas W. Human Language Technology Center of Excellence UMIACS Laboratory for Computational Linguistics and Information Processing University of Maryland College ParkMD20742 United States Department of Computer Science The iSchool College of Information Studies

This paper presents a MapReduce algorithm for computing pairwise document similarity in large document collections. MapReduce is an attractive framework because it allows us to decompose the inner products involved in computing document similarity into separate multiplication and summation stages in a way that is well matched to efficient disk access patterns across several machines. On a collection consisting of approximately 900,000 newswire articles, our algorithm exhibits linear growth in running time and space in terms of the number of documents. © 2008 Association for Computational Linguistics.

关键词： MapReduce

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Computing word-pair antonymy

Computing word-pair antonymy

引用

2008 Conference on Empirical Methods in Natural language processing, EMNLP 2008, Co-located with AMTA 2008 and the International Workshop on Spoken language Translation

作者： Mohammad, Saif Dorr, Bonnie Hirst, Graeme Laboratory for Computational Linguistics and Information Processing Institute for Advanced Computer Studies University of Maryland United States Computer Science Human Language Technology Center of Excellence United States Department of Computer Science University of Toronto Canada

Knowing the degree of antonymy between words has widespread applications in natural language processing. Manually-created lexicons have limited coverage and do not include most semantically contrasting word pairs. We present a new automatic and empirical measure of antonymy that combines corpus statistics with the structure of a published thesaurus. The approach is evaluated on a set of closest-opposite questions, obtaining a precision of over 80%. Along the way, we discuss what humans consider antonymous and how antonymy manifests itself in utterances. © 2008 Association for Computational Linguistics.

关键词： Natural language processing systems

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Computing word-pair antonymy 08

Computing word-pair antonymy

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Proceedings of the Conference on Empirical Methods in Natural language processing

作者： Saif Mohammad Bonnie Dorr Graeme Hirst University of Maryland Human Language Technology Center of Excellence University of Toronto

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Resolving personal names in email using context expansion

Resolving personal names in email using context expansion

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46th Annual Meeting of the Association for Computational Linguistics: human language Technologies, ACL-08: HLT

作者： Elsayed, Tamer Oard, Douglas W. Namata, Galileo Department of Computer Science Human Language Technology Center of Excellence University of Maryland College Park MD 20742 United States University of Maryland College Park MD 20742 United States

ISBN: (纸本)9781932432046

This paper describes a computational approach to resolving the true referent of a named mention of a person in the body of an email. A generative model of mention generation is used to guide mention resolution. Results on three relatively small collections indicate that the accuracy of this approach compares favorably to the best known techniques, and results on the full CMU Enron collection indicate that it scales well to larger collections. © 2008 Association for Computational Linguistics.

关键词： Electronic mail

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Combining speech retrieval results with generalized additive models

Combining speech retrieval results with generalized additive...

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46th Annual Meeting of the Association for Computational Linguistics: human language Technologies, ACL-08: HLT

作者： Olsson, J. Scott Oard, Douglas W. Dept. of Mathematics/AMSC University of Maryland UMD College Park MD 20742 United States Human Language Technology Center of Excellence College of Information Studies John Hopkins University Baltimore MD 21211 United States

ISBN: (纸本)9781932432046

Rapid and inexpensive techniques for automatic transcription of speech have the potential to dramatically expand the types of content to which information retrieval techniques can be productively applied, but limitations in accuracy and robustness must be overcome before that promise can be fully realized. Combining retrieval results from systems built on various errorful representations of the same collection offers some potential to address these challenges. This paper explores that potential by applying Generalized Additive Models to optimize the combination of ranked retrieval results obtained using transcripts produced automatically for the same spoken content by substantially different recognition systems. Topic-averaged retrieval effectiveness better than any previously reported for the same collection was obtained, and even larger gains are apparent when using an alternative measure emphasizing results on the most difficult topics. © 2008 Association for Computational Linguistics.

关键词： Information retrieval

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Pairwise document similarity in large collections with MapReduce

Pairwise document similarity in large collections with MapRe...

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46th Annual Meeting of the Association for Computational Linguistics: human language Technologies, ACL-08: HLT

作者： Elsayed, Tamer Lin, Jimmy Oard, Douglas W. Department of Computer Science Human Language Technology Center of Excellenc University of Maryland College Park MD 20742 United States UMIACS Laboratory for Computational Linguistics and Information Processing College of Information Studies University of Maryland College Park MD 20742 United States

ISBN: (纸本)9781932432046

关键词： MapReduce

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Robust speech Recognition Using Noise-Cluster HMM Interpolation

Robust Speech Recognition Using Noise-Cluster HMM Interpolat...

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2008 9th International Conference on Signal processing(ICSP’2008)

作者： Nattanun Thatphithakkul Boontee Kruatrachue Chai Wutiwiwatchai Sanparith Marukatat Vataya Boonpiam Computer Engineering Department Faculty of EngineeringKing Mongkut's Institute of Technology LadkrabangBangkok10520Thailand Speech Technology Section Human Language Technology Laboratory National Electronics and Computer Technology CenterPathumthani12120Thailand

This paper proposes a novel approach called noisecluster HMM interpolation for robust speech *** approach helps alleviating the problem of speech recognition under noisy environments not trained in the *** this method,a new HMM is interpolated from existing noisy-speech HMMs that are best matched to the input *** process is performed on-the-fly with an acceptable delay time and,hence,no need to prepare and store the final model in *** weights among HMMs can be determined by either a direct or a tree-structured *** focusing on speech in unseen noisy-environments,the proposed method obviously outperforms a baseline system whose acoustic model for such unseen environment is selected from a tree structure.

关键词： HMM Robust speech Recognition Using Noise-Cluster HMM Interpolation Cluster

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Perceptual MVDR-based Cepstral Coefficients (PMCCs) for high accuracy speech recognition 8

Perceptual MVDR-based Cepstral Coefficients (PMCCs) for high...

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8th European Conference on speech Communication and technology, EUROspeech 2003

作者： Yapanel, Umit H. Dharanipragada, Satya Hansen, John H.L. Robust Speech Processing Group Center for Spoken Language Research University of Colorado at Boulder BoulderCO80309 United States Human Language Technologies IBM TJ Watson Research Center Yorktown HeightsNY10598 United States

This paper describes an accurate feature representation for continuous clean speech recognition. The main components of the technique involve performing a moderate order Linear Predictive (LP) analysis and computing the Minimum Variance Distortionless Response (MVDR) spectrum from these LP coefficients. This feature representation, PMCCs, was earlier shown to yield superior performance over MFCCs for different noise conditions with emphasis on car noise [1]. The performance improvement was then attributed to better spectrum and envelope modeling properties of the MVDR methodology. This study shows that the representation is also quite efficient for clean speech recognition. In fact, PMCCs are shown to be a more accurate envelope representation and reduce speaker variability. This, in turn, yields a 12.8% relative word error rate (WER) reduction on the coombination of Wall Street Journal (WSJ) Nov?92 dev/eval sets with respect to the MFCCs. Accurate envelope modeling and reduction in the speaker variability also lead to faster decoding, based on efficient pruning in the search stage. The total gain in the decoding speed is 22.4%, relative to the standard MFCC features. It is also shown that PMCCs are not very demanding in terms of computation when compared to MFCCs. Therefore, we conclude that PMCC feature extraction scheme is a better representation of clean speech as well as noisy speech than MFCC scheme.

关键词： speech recognition

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