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AI-KU: Using Substitute Vectors and Co-Occurrence Modeling For Word Sense Induction and Disambiguation
Osman Başkaya
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Enis Sert
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Volkan Cirik
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Deniz Yuret
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Paper Details:
Month: June
Year: 2013
Location: Atlanta, Georgia, USA
Venue:
*SEMEVAL |
Citations
URL
Word Sense Induction Using Lexical Chain based Hypergraph Model
Tao Qian
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Donghong Ji
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Mingyao Zhang
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Chong Teng
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Congling Xia
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Structured Generative Models of Continuous Features for Word Sense Induction
Alexandros Komninos
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Suresh Manandhar
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Context-Dependent Sense Embedding
Lin Qiu
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Kewei Tu
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Yong Yu
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Word Sense Induction with Neural biLM and Symmetric Patterns
Asaf Amrami
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Yoav Goldberg
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Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation
Alexander Panchenko
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Eugen Ruppert
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Stefano Faralli
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Simone Paolo Ponzetto
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Chris Biemann
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One Million Sense-Tagged Instances for Word Sense Disambiguation and Induction
Kaveh Taghipour
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Hwee Tou Ng
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Modeling Word Meaning in Context with Substitute Vectors
Oren Melamud
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Ido Dagan
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Jacob Goldberger
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A Sense-Topic Model for Word Sense Induction with Unsupervised Data Enrichment
Jing Wang
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Mohit Bansal
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Kevin Gimpel
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Brian D. Ziebart
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Clement T. Yu
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AI-KU: Using Co-Occurrence Modeling for Semantic Similarity
Osman Başkaya
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Reducing VSM data sparseness by generalizing contexts: application to health text mining
Amandine Périnet
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Thierry Hamon
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Generalising and Normalising Distributional Contexts to Reduce Data Sparsity: Application to Medical Corpora
Amandine Périnet
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Thierry Hamon
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Neural context embeddings for automatic discovery of word senses
Mikael Kågebäck
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Fredrik Johansson
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Richard Johansson
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Devdatt Dubhashi
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Making Sense of Word Embeddings
Maria Pelevina
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Nikolay Arefiev
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Chris Biemann
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Alexander Panchenko
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http://goo.gl/jPTZQ
http://wacky.sslmit.unibo.it
Field Of Study
Task
Tagging
Word Sense Disambiguation
Language
English
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