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Modeling Latent Biographic Attributes in Conversational Genres

Modeling Latent Biographic Attributes in Conversational Genr...

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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： Garera, Nikesh Yarowsky, David Department of Computer Science Johns Hopkins University Human Language Technology Center of Excellence BaltimoreMD United States

ISBN: (纸本)9781617382581

This paper presents and evaluates several original techniques for the latent classification of biographic attributes such as gender, age and native language, in diverse genres (conversation transcripts, email) and languages (Arabic, English). First, we present a novel partner-sensitive model for extracting biographic attributes in conversations, given the differences in lexical usage and discourse style such as observed between same-gender and mixed-gender conversations. Then, we explore a rich variety of novel sociolinguistic and discourse-based features, including mean utterance length, passive/active usage, percentage domination of the conversation, speaking rate and filler word usage. Cumulatively up to 20% error reduction is achieved relative to the standard Boulis and Ostendorf (2005) algorithm for classifying individual conversations on Switchboard, and accuracy for gender detection on the Switchboard corpus (aggregate) and Gulf Arabic corpus exceeds 95%. © 2009 ACL and AFNLP.

关键词： Natural language processing systems

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Fluency, Adequacy, or HTER? Exploring Different human Judgments with a Tunable MT Metric 4

Fluency, Adequacy, or HTER? Exploring Different Human Judgme...

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4th Workshop on Statistical Machine Translation, WMT 2009, immediately preceding the 12th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2009

作者： Snover, Matthew Madnani, Nitin Dorr, Bonnie J. Schwartz, Richard Laboratory For Computational Linguistics And Information Processing Institute For Advanced Computer Studies University of Maryland College Park United States Human Language Technology Center of Excellence Bbn Technologies

Automatic Machine Translation (MT) evaluation metrics have traditionally been evaluated by the correlation of the scores they assign to MT output with human judgments of translation performance. Different types of human judgments, such as Fluency, Adequacy, and HTER, measure varying aspects of MT performance that can be captured by automatic MT metrics. We explore these differences through the use of a new tunable MT metric: TER-Plus, which extends the Translation Edit Rate evaluation metric with tunable parameters and the incorporation of morphology, synonymy and paraphrases. TER-Plus was shown to be one of the top metrics in NIST's Metrics MATR 2008 Challenge, having the highest average rank in terms of Pearson and Spearman correlation. Optimizing TER-Plus to different types of human judgments yields significantly improved correlations and meaningful changes in the weight of different types of edits, demonstrating significant differences between the types of human judgments. ©2009 Association for Computational Linguistics.

关键词： Machine translation

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Improving a simple bigram HMM part-of-speech tagger by latent annotation and self-training

Improving a simple bigram HMM part-of-speech tagger by laten...

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2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL-HLT 2009

作者： Huang, Zhongqiang Eidelman, Vladimir Harper, Mary Laboratory for Computational Linguistics and Information Processing Institute for Advanced Computer Studies University of Maryland College Park United States Human Language Technology Center of Excellence Johns Hopkins University United States

ISBN: (纸本)9781932432428

In this paper, we describe and evaluate a bigram part-of-speech (POS) tagger that uses latent annotations and then investigate using additional genre-matched unlabeled data for self-training the tagger. The use of latent annotations substantially improves the performance of a baseline HMM bigram tagger, outperforming a trigram HMM tagger with sophisticated smoothing. The performance of the latent tagger is further enhanced by self-training with a large set of unlabeled data, even in situations where standard bigram or trigram taggers do not benefit from self-training when trained on greater amounts of labeled training data. Our best model obtains a state-of-the-art Chinese tagging accuracy of 94.78% when evaluated on a representative test set of the Penn Chinese Treebank 6.0. © 2009 Association for Computational Linguistics

关键词： Computational linguistics

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A joint language model with fine-grain syntactic tags

A joint language model with fine-grain syntactic tags

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2009 Conference on Empirical Methods in Natural language processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009

作者： Filimonov, Denis Harper, Mary Laboratory for Computational Linguistics and Information Processing Institute for Advanced Computer Studies University of Maryland College Park United States Human Language Technology Center of Excellence Johns Hopkins University United States

We present a scalable joint language model designed to utilize fine-grain syntactic tags. We discuss challenges such a design faces and describe our solutions that scale well to large tagsets and corpora. We advocate the use of relatively simple tags that do not require deep linguistic knowledge of the language but provide more structural information than POS tags and can be derived from automatically generated parse trees - a combination of properties that allows easy adoption of this model for new languages. We propose two fine-grain tagsets and evaluate our model using these tags, as well as POS tags and SuperARV tags in a speech recognition task and discuss future directions. © 2009 ACL and AFNLP.

关键词： Syntactics

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Self-training PCFG grammars with latent annotations across languages

Self-training PCFG grammars with latent annotations across l...

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2009 Conference on Empirical Methods in Natural language processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009

作者： Huang, Zhongqiang Harper, Mary Laboratory for Computational Linguistics and Information Processing Institute for Advanced Computer Studies University of Maryland College Park United States Human Language Technology Center of Excellence Johns Hopkins University United States

We investigate the effectiveness of self-training PCFG grammars with latent annotations (PCFG-LA) for parsing languages with different amounts of labeled training data. Compared to Charniak's lexicalized parser, the PCFG-LA parser was more effectively adapted to a language for which parsing has been less well developed (i.e., Chinese) and benefited more from self-training. We show for the first time that self-training is able to significantly improve the performance of the PCFG-LA parser, a single generative parser, on both small and large amounts of labeled training data. Our approach achieves state-of-the-art parsing accuracies for a single parser on both English (91.5%) and Chinese (85.2%). © 2009 ACL and AFNLP.

关键词： Natural language processing systems

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MDCT for encoding residual signals in frequency domain linear prediction

MDCT for encoding residual signals in frequency domain linea...

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127th Audio Engineering Society Convention 2009

作者： Ganapathy, Sriram Motlicek, Petr Hermansky, Hynek Department of Electrical and Computer Engineering Human Language Technology Center of Excellence Johns Hopkins University United States Idiap Research Institute Martigny Switzerland

ISBN: (纸本)9781615677122

Frequency domain linear prediction (FDLP) uses autoregressive models to represent Hilbert envelopes of relatively long segments of speech/audio signals. Although the basic FDLP audio codec achieves good quality of the reconstructed signal at high bit-rates, there is a need for scaling to lower bit-rates without degrading the reconstruction quality. Here, we present a method for improving the compression efficiency of the FDLP codec by the application of the modified discrete cosine transform (MDCT) for encoding the FDLP residual signals. In the subjective and objective quality evaluations, the proposed FDLP codec provides competent quality of reconstructed signal compared to the state-of-the-art audio codecs for the 32 - 64 kbps range.

关键词： Signal encoding

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Cross-document coreference resolution: A key technology for learning by reading

Cross-document coreference resolution: A key technology for ...

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Learning by Reading and Learning to Read - Papers from the AAAI Spring Symposium

作者： Mayfield, James Alexander, David Dorr, Bonnie Eisner, Jason Elsayed, Tamer Finin, Tim Fink, Clay Freedman, Marjorie Garera, Nikesh Mcnamee, Paul Mohammad, Saif Oard, Douglas Piatko, Christine Sayeed, Asad Syed, Zareen Weischedel, Ralph Xu, Tan Yarowsky, David Human Language Technology Center of Excellence 810 WymanParkDrive Baltimore MD United States 1211 University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9781577354147

Automatic knowledge base population from text is an important technology for a broad range of approaches to learning by reading. Effective automated knowledge base population depends critically upon coreference resolution of entities across sources. Use of a wide range of features, both those that capture evidence for entity merging and those that argue against merging, can significantly improve machine learning-based cross-document coreference resolution. Results from the Global Entity Detection and Recognition task of the NIST Automated Content Extraction (ACE) 2008 evaluation support this conclusion.

关键词： Merging

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Self-supervised discriminative training of statistical language models

Self-supervised discriminative training of statistical langu...

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IEEE Workshop on Automatic speech Recognition and Understanding

作者： Puyang Xu Damianos Karakos Sanjeev Khudanpur Department of Electrical and Computer Engineering Center for Language and Speech Processing USA Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD USA Johns Hopkins University Baltimore MD US

ISBN: (纸本)9781424454785

A novel self-supervised discriminative training method for estimating language models for automatic speech recognition (ASR) is proposed. Unlike traditional discriminative training methods that require transcribed speech, only untranscribed speech and a large text corpus is required. An exponential form is assumed for the language model, as done in maximum entropy estimation, but the model is trained from the text using a discriminative criterion that targets word confusions actually witnessed in first-pass ASR output lattices. Specifically, model parameters are estimated to maximize the likelihood ratio between words w in the text corpus and w's cohorts in the test speech, i.e. other words that w competes with in the test lattices. Empirical results are presented to demonstrate statistically significant improvements over a 4-gram language model on a large vocabulary ASR task.

关键词： Automatic speech recognition Natural languages Lattices Maximum likelihood estimation humans Testing speech recognition speech processing Entropy Parameter estimation

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Fluency, adequacy, or HTER?: exploring different human judgments with a tunable MT metric 09

Fluency, adequacy, or HTER?: exploring different human judgm...

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Proceedings of the Fourth Workshop on Statistical Machine Translation

作者： Matthew Snover Nitin Madnani Bonnie J. Dorr Richard Schwartz University of Maryland College Park University of Maryland College Park amd Human Language Technology Center of Excellence Human Language Technology Center of Excellence and BBN Technologies

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Using wikitology for cross-document entity coreference resolution

Using wikitology for cross-document entity coreference resol...

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Learning by Reading and Learning to Read - Papers from the AAAI Spring Symposium

作者： Finin, Tim Syed, Zareen Mayfield, James Mcnamee, Paul Piatko, Christine University of Maryland Baltimore County Baltimore MD 21250 United States Johns Hopkins University Human Language Technology Center of Excellence Baltimore MD 21211 United States

ISBN: (纸本)9781577354147

We describe the use of the Wikitology knowledge base as a resource for a variety of applications with special focus on a cross-document entity coreference resolution task. This task involves recognizing when entities and relations mentioned in different documents refer to the same object or relation in the world. Wikitology is a knowledge base system constructed with material from Wikipedia, DBpedia and Freebase that includes both unstructured text and semi-structured information. Wikitology was used to define features that were part of a system implemented by the Johns Hopkins University human language technology center of excellence for the 2008 Automatic Content Extraction cross-document coreference resolution evaluation organized by National Institute of Standards and technology.

关键词： Knowledge based systems

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