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Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics Short Papers HLT-NAACL 2006 Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics Short Papers Robert C. Moore, General Chair Jeff Bilmes, Jennifer Chu-Carroll and Mark Sanderson Program Committee Chairs June 4-9, 2006 New York, New York, USA Published by the Association for Computational Linguistics http://www.aclweb.org Production and Manufacturing by Omnipress Inc. 2600 Anderson Street Madison, WI 53704 c 2006 The Association for Computational Linguistics Order copies of this and other ACL proceedings from: Association for Computational Linguistics (ACL) 209 N. Eighth Street Stroudsburg, PA 18360 USA Tel: +1-570-476-8006 Fax: +1-570-476-0860 [email protected] ii Table of Contents Factored Neural Language Models Andrei Alexandrescu and Katrin Kirchhoff. .1 The MILE Corpus for Less Commonly Taught Languages Alison Alvarez, Lori Levin, Robert Frederking, Simon Fung, Donna Gates and Jeff Good. .5 Museli: A Multi-Source Evidence Integration Approach to Topic Segmentation of Spontaneous Dialogue Jaime Arguello and Carolyn Rose . 9 Measuring Semantic Relatedness Using People and WordNet Beata Beigman Klebanov . 13 Thai Grapheme-Based Speech Recognition Paisarn Charoenpornsawat, Sanjika Hewavitharana and Tanja Schultz . 17 Class Model Adaptation for Speech Summarisation Pierre Chatain, Edward Whittaker, Joanna Mrozinski and Sadaoki Furui . 21 Semi-supervised Relation Extraction with Label Propagation Jinxiu Chen, Donghong Ji, Chew Lim Tan and Zhengyu Niu . 25 Temporal Classification of Text and Automatic Document Dating Angelo Dalli . 29 Answering the question you wish they had asked: The impact of paraphrasing for Question Answering Pablo Duboue and Jennifer Chu-Carroll . 33 Gesture Improves Coreference Resolution Jacob Eisenstein and Randall Davis . 37 Spectral Clustering for Example Based Machine Translation Rashmi Gangadharaiah, Ralf Brown and Jaime Carbonell . 41 A Finite-State Model of Georgian Verbal Morphology Olga Gurevich . 45 Arabic Preprocessing Schemes for Statistical Machine Translation Nizar Habash and Fatiha Sadat . 49 Agreement/Disagreement Classification: Exploiting Unlabeled Data using Contrast Classifiers Sangyun Hahn, Richard Ladner and Mari Ostendorf . 53 OntoNotes: The 90% Solution Eduard Hovy, Mitchell Marcus, Martha Palmer, Lance Ramshaw and Ralph Weischedel. 57 Investigating Cross-Language Speech Retrieval for a Spontaneous Conversational Speech Collection Diana Inkpen, Muath Alzghool, Gareth Jones and Douglas Oard . 61 iii Evaluating Centering for Sentence Ordering in Two New Domains Nikiforos Karamanis . 65 MMR-based Active Machine Learning for Bio Named Entity Recognition Seokhwan Kim, Yu Song, Kyungduk Kim, Jeong-Won Cha and Gary Geunbae Lee . 69 Early Deletion of Fillers In Processing Conversational Speech Matthew Lease and Mark Johnson . 73 Evaluation of Utility of LSA for Word Sense Discrimination Esther Levin, Mehrbod Sharifi and Jerry Ball. .77 Initial Study on Automatic Identification of Speaker Role in Broadcast News Speech YangLiu................................................................................. 81 Automatic Recognition of Personality in Conversation Franois Mairesse and Marilyn Walker . 85 Summarizing Speech Without Text Using Hidden Markov Models Sameer Maskey and Julia Hirschberg . 89 NER Systems that Suit User’s Preferences: Adjusting the Recall-Precision Trade-off for Entity Extraction Einat Minkov, Richard Wang, Anthony Tomasic and William Cohen . 93 Syntactic Kernels for Natural Language Learning: the Semantic Role Labeling Case Alessandro Moschitti . 97 Accurate Parsing of the Proposition Bank Gabriele Musillo and Paola Merlo . 101 Using Semantic Authoring for Blissymbols Communication Boards Yael Netzer and Michael Elhadad . 105 Extracting Salient Keywords from Instructional Videos Using Joint Text, Audio and Visual Cues Youngja Park and Ying Li . 109 Exploiting Variant Corpora for Machine Translation Michael Paul and Eiichiro Sumita. .113 Quantitative Methods for Classifying Writing Systems Gerald Penn and Travis Choma . 117 Computational Modelling of Structural Priming in Dialogue David Reitter, Frank Keller and Johanna D. Moore . 121 Story Segmentation of Broadcast News in English, Mandarin and Arabic Andrew Rosenberg and Julia Hirschberg. .125 Parser Combination by Reparsing Kenji Sagae and Alon Lavie . 129 iv Using Phrasal Patterns to Identify Discourse Relations Manami Saito, Kazuhide Yamamoto and Satoshi Sekine . 133 Weblog Classification for Fast Splog Filtering: A URL Language Model Segmentation Approach Franco Salvetti and Nicolas Nicolov . 137 Word Domain Disambiguation via Word Sense Disambiguation Antonio Sanfilippo, Stephen Tratz and Michelle Gregory . 141 Selecting relevant text subsets from web-data for building topic specific language models Abhinav Sethy, Panayiotis Georgiou and Shrikanth Narayanan . 145 A Comparison of Tagging Strategies for Statistical Information Extraction Christian Siefkes . 149 Unsupervised Induction of Modern Standard Arabic Verb Classes Neal Snider and Mona Diab . 153 Sentence Planning for Realtime Navigational Instruction Laura Stoia, Donna Byron, Darla Shockley and Eric Fosler-Lussier . 157 Using the Web to Disambiguate Acronyms Eiichiro Sumita and Fumiaki Sugaya . 161 Word Pronunciation Disambiguation using the Web Eiichiro Sumita and Fumiaki Sugaya . 165 Illuminating Trouble Tickets with Sublanguage Theory Svetlana Symonenko, Steven Rowe and Elizabeth D. Liddy . 169 Evolving optimal inspectable strategies for spoken dialogue systems Dave Toney, Johanna Moore and Oliver Lemon . 173 Lycos Retriever: An Information Fusion Engine Brian Ulicny. .177 Improved Affinity Graph Based Multi-Document Summarization Xiaojun Wan and Jianwu Yang . 181 A Maximum Entropy Framework that Integrates Word Dependencies and Grammatical Relations for Read- ing Comprehension Kui Xu, Helen Meng and Fuliang Weng . 185 BioEx: A Novel User-Interface that Accesses Images from Abstract Sentences Hong Yu and Minsuk Lee . 189 Subword-based Tagging by Conditional Random Fields for Chinese Word Segmentation Ruiqiang Zhang, Genichiro Kikui and Eiichiro Sumita . 193 v Comparing the roles of textual, acoustic and spoken-language features on spontaneous-conversation sum- marization Xiaodan Zhu and Gerald Penn . 197 Bridging the Inflection Morphology Gap for Arabic Statistical Machine Translation Andreas Zollmann, Ashish Venugopal and Stephan Vogel . 201 vi Factored Neural Language Models Andrei Alexandrescu Katrin Kirchhoff Department of Comp. Sci. Eng. Department of Electrical Engineering University of Washington University of Washington [email protected] [email protected] Abstract Though this may be sufficient for applications that use a closed vocabulary, the current trend of porting We present a new type of neural proba- systems to a wider range of languages (esp. highly- bilistic language model that learns a map- inflected languages such as Arabic) calls for dy- ping from both words and explicit word namic dictionary expansion and the capability of as- features into a continuous space that is signing probabilities to newly added words without then used for word prediction. Addi- having seen them in the training data. Here, we in- tionally, we investigate several ways of troduce a novel type of NLM that improves gener- deriving continuous word representations alization by using vectors of word features (stems, for unknown words from those of known affixes, etc.) as input, and we investigate deriving words. The resulting model significantly continuous representations for unknown words from reduces perplexity on sparse-data tasks those of known words. when compared to standard backoff mod- els, standard neural language models, and 2 Neural Language Models factored language models. wn−2 |V| rows d columns P(w | w ,w ) t t−1 t−2 1 Introduction M h W W ih ho Neural language models (NLMs) (Bengio et al., V = vocabulary i w d = continuous space size o 2000) map words into a continuous representation n−1 space and then predict the probability of a word Figure 1: NLM architecture. Each word.
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