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Dynamic and Static Prototype Vectors for Semantic Composition
Siva Reddy
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Ioannis Klapaftis
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Diana McCarthy
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Suresh Manandhar
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Paper Details:
Month: November
Year: 2011
Location: Chiang Mai, Thailand
Venue:
IJCNLP |
Citations
URL
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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A Markovian approach to distributional semantics with application to semantic compositionality
Édouard Grave
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Guillaume Obozinski
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Francis Bach
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Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space
Arvind Neelakantan
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Jeevan Shankar
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Alexandre Passos
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Andrew McCallum
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Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning
Jianpeng Cheng
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Dimitri Kartsaklis
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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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Resolving Lexical Ambiguity in Tensor Regression Models of Meaning
Dimitri Kartsaklis
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Nal Kalchbrenner
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Mehrnoosh Sadrzadeh
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Compositional Semantics using Feature-Based Models from WordNet
Pablo Gamallo
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Martín Pereira-Fariña
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One Representation per Word - Does it make Sense for Composition?
Thomas Kober
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Julie Weeds
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John Wilkie
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Jeremy Reffin
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David Weir
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http://www.sketchengine.co
Field Of Study
Linguistic Trends
Lexical Semantics
Language
Chinese
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