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A Probabilistic Model for Learning Multi-Prototype Word Embeddings
Fei Tian
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Hanjun Dai
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Jiang Bian
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Bin Gao
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Rui Zhang
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Enhong Chen
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Tie-Yan Liu
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Paper Details:
Month: August
Year: 2014
Location: Dublin, Ireland
Venue:
COLING |
Citations
URL
Inducing Bilingual Lexica From Non-Parallel Data With Earth Mover’s Distance Regularization
Meng Zhang
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Yang Liu
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Huanbo Luan
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Yiqun Liu
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Maosong Sun
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Model-Free Context-Aware Word Composition
Bo An
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Xianpei Han
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Le Sun
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A Unified Model for Word Sense Representation and Disambiguation
Xinxiong Chen
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Zhiyuan Liu
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Maosong Sun
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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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Supervised Distributional Hypernym Discovery via Domain Adaptation
Luis Espinosa-Anke
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Jose Camacho-Collados
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Claudio Delli Bovi
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Horacio Saggion
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Solving Verbal Questions in IQ Test by Knowledge-Powered Word Embedding
Huazheng Wang
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Fei Tian
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Bin Gao
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Chengjieren Zhu
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Jiang Bian
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Tie-Yan Liu
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MUSE: Modularizing Unsupervised Sense Embeddings
Guang-He Lee
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Yun-Nung Chen
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Spot the Odd Man Out: Exploring the Associative Power of Lexical Resources
Gabriel Stanovsky
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Mark Hopkins
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On Modeling Sense Relatedness in Multi-prototype Word Embedding
Yixin Cao
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Jiaxin Shi
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Juanzi Li
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Zhiyuan Liu
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Chengjiang Li
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Embedding Words and Senses Together via Joint Knowledge-Enhanced Training
Massimiliano Mancini
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Jose Camacho-Collados
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Ignacio Iacobacci
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Roberto Navigli
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Multi-prototype Chinese Character Embedding
Yanan Lu
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Yue Zhang
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Donghong Ji
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Best of Both Worlds: Making Word Sense Embeddings Interpretable
Alexander Panchenko
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Ontologically Grounded Multi-sense Representation Learning for Semantic Vector Space Models
Sujay Kumar Jauhar
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Chris Dyer
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Eduard Hovy
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Bilingual Learning of Multi-sense Embeddings with Discrete Autoencoders
Simon Šuster
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Ivan Titov
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Gertjan van Noord
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Retrofitting Sense-Specific Word Vectors Using Parallel Text
Allyson Ettinger
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Philip Resnik
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Marine Carpuat
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AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes
Sascha Rothe
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Hinrich Schütze
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Improving Distributed Representation of Word Sense via WordNet Gloss Composition and Context Clustering
Tao Chen
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Ruifeng Xu
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Yulan He
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Xuan Wang
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Towards a Seamless Integration of Word Senses into Downstream NLP Applications
Mohammad Taher Pilehvar
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Jose Camacho-Collados
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Roberto Navigli
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Nigel Collier
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Improved Word Representation Learning with Sememes
Yilin Niu
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Ruobing Xie
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Zhiyuan Liu
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Maosong Sun
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Probabilistic FastText for Multi-Sense Word Embeddings
Ben Athiwaratkun
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Andrew Wilson
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Anima Anandkumar
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A Mixture Model for Learning Multi-Sense Word Embeddings
Dai Quoc Nguyen
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Dat Quoc Nguyen
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Ashutosh Modi
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Stefan Thater
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Manfred Pinkal
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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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Learning to Embed Words in Context for Syntactic Tasks
Lifu Tu
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Kevin Gimpel
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Karen Livescu
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Efficient Graph-based Word Sense Induction by Distributional Inclusion Vector Embeddings
Haw-Shiuan Chang
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Amol Agrawal
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Ananya Ganesh
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Anirudha Desai
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Vinayak Mathur
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Alfred Hough
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Andrew McCallum
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http://creativecommons.org/licenses/by/4.0/
Field Of Study
Linguistic Trends
Embeddings
Task
Information Retrieval
Language
English
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