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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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Applications of signal analysis using autoregressive models for amplitude modulation

Applications of signal analysis using autoregressive models ...

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2009 IEEE Workshop on Applications of Signal processing to Audio and Acoustics, WASPAA 2009

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

ISBN: (纸本)9781424436798

Frequency Domain Linear Prediction (FDLP) represents an efficient technique for representing the long-term amplitude modulations (AM) of speech/audio signals using autoregressive models. For the proposed analysis technique, relatively long temporal segments (1000 ms) of the input signal are decomposed into a set of sub-bands. FDLP is applied on each sub-band to model the temporal envelopes. The residual of the linear prediction represents the frequency modulations (FM) in the sub-band signal. In this paper, we present several applications of the proposed AM-FM decomposition technique for a variety of tasks like wide-band audio coding, speech recognition in reverberant environments and robust feature extraction for phoneme recognition. ©2009 IEEE.

关键词： Amplitude modulation

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Taking into account the differences between actively and passively acquired data: The case of active learning with support vector machines for imbalanced datasets

Taking into account the differences between actively and pas...

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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： Bloodgood, Michael Vijay-Shanker, K. Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD21211 United States Computer and Information Sciences Department University of Delaware NewarkDE19716 United States

ISBN: (纸本)9781932432428

Actively sampled data can have very different characteristics than passively sampled data. Therefore, it’s promising to investigate using different inference procedures during AL than are used during passive learning (PL). This general idea is explored in detail for the focused case of AL with cost-weighted SVMs for imbalanced data, a situation that arises for many HLT tasks. The key idea behind the proposed InitPA method for addressing imbalance is to base cost models during AL on an estimate of overall corpus imbalance computed via a small unbiased sample rather than the imbalance in the labeled training data, which is the leading method used during PL. © 2009 Association for Computational Linguistics

关键词： Support vector machines

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Using citations to generate surveys of scientific paradigms 09

Using citations to generate surveys of scientific paradigms

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Proceedings of human language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

作者： Saif Mohammad Bonnie Dorr Melissa Egan Ahmed Hassan Pradeep Muthukrishan Vahed Qazvinian Dragomir Radev David Zajic University of Maryland and Human Language Technology Center of Excellence University of Maryland University of Michigan University of Maryland and Center for Advanced Study of Language

ISBN: (纸本)9781932432411

The number of research publications in various disciplines is growing exponentially. Researchers and scientists are increasingly finding themselves in the position of having to quickly understand large amounts of technical material. In this paper we present the first steps in producing an automatically generated, readily consumable, technical survey. Specifically we explore the combination of citation information and summarization techniques. Even though prior work (Teufel et al., 2006) argues that citation text is unsuitable for summarization, we show that in the framework of multi-document survey creation, citation texts can play a crucial role.

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Modeling latent biographic attributes in conversational genres 09

Modeling latent biographic attributes in conversational genr...

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Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural language processing of the AFNLP: Volume 2 - Volume 2

作者： Nikesh Garera David Yarowsky Johns Hopkins University Human Language Technology Center of Excellence Baltimore MD

ISBN: (纸本)9781932432466

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%.

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Repetition and language Models and Comparable Corpora

BUCC 2009 - 2nd Workshop on Building and Using Comparable Co...

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BUCC 2009 - 2nd Workshop on Building and Using Comparable Corpora: From Parallel to Non-Parallel Corpora at the ACL-IJCNLP 2009 - Proceedings 2009年 1-1页

作者： Church, Ken Human Language Technology Center of Excellence Johns Hopkins University United States

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Translation corpus source and size in bilingual retrieval

Translation corpus source and size in bilingual retrieval

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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： McNamee, Paul Mayfield, James Nicholas, Charles Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD21218 United States Dept. of Computer Science and Electrical Engineering UMBC BaltimoreMD21250 United States

ISBN: (纸本)9781932432428

This paper explores corpus-based bilingual retrieval where the translation corpora used vary by source and size. We find that the quality of translation alignments and the domain of the bitext are important. In some settings these factors are more critical than corpus size. We also show that judicious choice of tokenization can reduce the amount of bitext required to obtain good bilingual retrieval performance. © 2009 Association for Computational Linguistics

关键词： Translation (languages)

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