TAILIEUCHUNG - Báo cáo khoa học: "Topic Models for Word Sense Disambiguation and Token-based Idiom Detection"

This paper presents a probabilistic model for sense disambiguation which chooses the best sense based on the conditional probability of sense paraphrases given a context. We use a topic model to decompose this conditional probability into two conditional probabilities with latent variables. We propose three different instantiations of the model for solving sense disambiguation problems with different degrees of resource availability. | Topic Models for Word Sense Disambiguation and Token-based Idiom Detection Linlin Li Benjamin Roth and Caroline Sporleder Saarland University Postfach 15 11 50 66041 Saarbriicken Germany linlin beroth csporled @ Abstract This paper presents a probabilistic model for sense disambiguation which chooses the best sense based on the conditional probability of sense paraphrases given a context. We use a topic model to decompose this conditional probability into two conditional probabilities with latent variables. We propose three different instantiations of the model for solving sense disambiguation problems with different degrees of resource availability. The proposed models are tested on three different tasks coarse-grained word sense disambiguation fine-grained word sense disambiguation and detection of literal vs. nonliteral usages of potentially idiomatic expressions. In all three cases we outperform state-of-the-art systems either quantitatively or statistically significantly. 1 Introduction Word sense disambiguation WSD is the task of automatically determining the correct sense for a target word given the context in which it occurs. WSD is an important problem in NLP and an essential preprocessing step for many applications including machine translation question answering and information extraction. However WSD is a difficult task and despite the fact that it has been the focus of much research over the years state-of-the-art systems are still often not good enough for real-world applications. One major factor that makes WSD difficult is a relative lack of manually annotated corpora which hampers the performance of supervised systems. To address this problem there has been a significant amount of work on unsupervised WSD that does not require manually sense-disambiguated training data see McCarthy 2009 for an overview . Recently several researchers have experimented with topic models Brody and Lapata 2009 Boyd-Graber et al. 2007 Boyd-Graber and

• Báo cáo khoa học
• Topic Models
• Word Sense Disambiguation
• Token based Idiom Detection
• báo cáo khoa học
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• BMC Bioinformatics
• Dynamic topic model
• Times series gene expression
• Topic modeling
• Latent dirichlet model
• Dynamic Translation Model Adaptation
• Vladimir Eidelman
• Authorship Attribution
• Author aware Topic Models
• Yanir Seroussi
• PCFGs Topic Models
• Adaptor Grammars and Learning Topical Collocations
• the Structure of Proper Names
• Automatic Labelling
• Jey Han Lau
• Identifying Word Translations
• Comparable Corpora
• Latent Topic Models
• Employing Topic Models
• Pattern based Semantic Class Discovery
• Mingjie Zhu
• xử lý ngôn ngữ tự nhiên
• Multi Document Summarization
• Sentence based Topic Models
• Dingding Wang
• Unsupervised Topic Identification
• Integrating Linguistic
• Visual Information Based on Hidden Markov Models
• mô hình ngôn ngữ
• Bilingual Topic AdMixture Models
• Word Alignment
• Bing Zhao
• Incorporating topic information
• sentiment analysis models
• Tony Mullen
• Statistical Models
• Topic Segmentation
• Jeffrey C
• Reynar
• Incorporating Lexical Priors
• Jagadeesh Jagarlamudi
• scientific reports
• model language
• process natural language
• Posterior inference
• Online maximum a posteriori estimation
• Large scale learning
• Streaming text collections
• Financial modeling
• Lecture Financial modeling
• Asset expected return
• Accrued interest
• Yield curve models
• Forward rates
• Financial statement simulation
• Cash flow
• Cash flow models
• Financial simulations
• Retirement ratios
• Changing asset allocation
• Sequence motifs
• Transcription factor binding sites
• Molecular biology
• DNA sequences
• User defined function
• Calculating cost of capital
• Portfolio models
• Estimating asset risk
• Asset risk
• Portfolio risk
• Computing portfolio risk