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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

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.

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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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Using word-sense disambiguation methods to classify web queries by intent

Using word-sense disambiguation methods to classify web quer...

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

作者： Pitler, Emily Church, Ken Computer and Information Science University of Pennsylvania Philadelphia PA 19104 United States Johns Hopkins University Human Language Technology Center of Excellence Baltimore MD 21211 United States

Three methods are proposed to classify queries by intent (CQI), e.g., navigational, informational, commercial, etc. Following mixed-initiative dialog systems, search engines should distinguish navigational queries where the user is taking the initiative from other queries where there are more opportunities for system initiatives (e.g., suggestions, ads). The query intent problem has a number of useful applications for search engines, affecting how many (if any) advertisements to display, which results to return, and how to arrange the results page. Click logs are used as a substitute for annotation. Clicks on ads are evidence for commercial intent;other types of clicks are evidence for other intents. We start with a simple Naïve Bayes baseline that works well when there is plenty of training data. When training data is less plentiful, we back off to nearby URLs in a click graph, using a method similar to Word-Sense Disambiguation. Thus, we can infer that designer trench is commercial because it is close to ***, which is known to be commercial. The baseline method was designed for precision and the backoff method was designed for recall. Both methods are fast and do not require crawling webpages. We recommend a third method, a hybrid of the two, that does no harm when there is plenty of training data, and generalizes better when there isn't, as a strong baseline for the CQI task. © 2009 ACL and AFNLP.

关键词： Search engines

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Generating high-coverage semantic orientation lexicons from overtly marked words and a thesaurus 09

Generating high-coverage semantic orientation lexicons from ...

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Proceedings of the 2009 Conference on Empirical Methods in Natural language processing: Volume 2 - Volume 2

作者： Saif Mohammad Cody Dunne Bonnie Dorr Laboratory for Computational Linguistics and Information Processing and Institute for Advanced Computer Studies and Human Language Technology Center of Excellence Human-Computer Interaction Lab and University of Maryland Laboratory for Computational Linguistics and Information Processing and Institute for Advanced Computer Studies and University of Maryland and Human Language Technology Center of Excellence

ISBN: (纸本)9781932432626

Sentiment analysis often relies on a semantic orientation lexicon of positive and negative words. A number of approaches have been proposed for creating such lexicons, but they tend to be computationally expensive, and usually rely on significant manual annotation and large corpora. Most of these methods use WordNet. In contrast, we propose a simple approach to generate a high-coverage semantic orientation lexicon, which includes both individual words and multi-word expressions, using only a Roget-like thesaurus and a handful of affixes. Further, the lexicon has properties that support the Polyanna Hypothesis. Using the General Inquirer as gold standard, we show that our lexicon has 14 percentage points more correct entries than the leading WordNet-based high-coverage lexicon (SentiWordNet). In an extrinsic evaluation, we obtain significantly higher performance in determining phrase polarity using our thesaurus-based lexicon than with any other. Additionally, we explore the use of visualization techniques to gain insight into the our algorithm beyond the evaluations mentioned above.

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A method for stopping active learning based on stabilizing predictions and the need for user-adjustable stopping

A method for stopping active learning based on stabilizing p...

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13th Conference on Computational Natural language Learning, CoNLL 2009

作者： Bloodgood, Michael Vijay-Shanker, K. Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD 21211 United States Computer and Information Sciences Department University of Delaware Newark DE 19716 United States

ISBN: (纸本)1932432299

A survey of existing methods for stopping active learning (AL) reveals the needs for methods that are: more widely applicable;more aggressive in saving annotations;and more stable across changing datasets. A new method for stopping AL based on stabilizing predictions is presented that addresses these needs. Furthermore, stopping methods are required to handle a broad range of different annotation/ performance tradeoff valuations. Despite this, the existing body of work is dominated by conservative methods with little (if any) attention paid to providing users with control over the behavior of stopping methods. The proposed method is shown to fill a gap in the level of aggressiveness available for stopping AL and supports providing users with control over stopping behavior. © 2009 Association for Computational Linguistics.

关键词： Computational linguistics

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Multi-class confidence weighted algorithms

Multi-class confidence weighted algorithms

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

作者： Crammer, Koby Dredze, Mark Kulesza, Alex Department of Computer and Information Science University of Pennsylvania Philadelphia PA 19104 United States Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD 21211 United States

The recently introduced online confidence-weighted (CW) learning algorithm for binary classification performs well on many binary NLP tasks. However, for multi-class problems CW learning updates and inference cannot be computed analytically or solved as convex optimization problems as they are in the binary case. We derive learning algorithms for the multi-class CW setting and provide extensive evaluation using nine NLP datasets, including three derived from the recently released New York Times corpus. Our best algorithm outperforms state-of-the-art online and batch methods on eight of the nine tasks. We also show that the confidence information maintained during learning yields useful probabilistic information at test time. © 2009 ACL and AFNLP.

关键词： Natural language processing systems

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