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Modeling and Learning Semantic Co-Compositionality through Prototype Projections and Neural Networks
Masashi Tsubaki
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Kevin Duh
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Masashi Shimbo
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Yuji Matsumoto
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Paper Details:
Month: October
Year: 2013
Location: Seattle, Washington, USA
Venue:
EMNLP |
SIG: SIGDAT
Citations
URL
A Neural Network Approach to Selectional Preference Acquisition
Tim Van de Cruys
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Jointly Learning Word Representations and Composition Functions Using Predicate-Argument Structures
Kazuma Hashimoto
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Pontus Stenetorp
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Makoto Miwa
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Yoshimasa Tsuruoka
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A Framework for Compiling High Quality Knowledge Resources From Raw Corpora
Gongye Jin
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Daisuke Kawahara
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Sadao Kurohashi
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Literal and Metaphorical Senses in Compositional Distributional Semantic Models
E.Dario Gutiérrez
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Ekaterina Shutova
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Tyler Marghetis
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Benjamin Bergen
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Learning Embeddings for Transitive Verb Disambiguation by Implicit Tensor Factorization
Kazuma Hashimoto
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Yoshimasa Tsuruoka
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Using Embedding Masks for Word Categorization
Stefan Ruseti
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Traian Rebedea
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Stefan Trausan-Matu
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A Vector Model for Type-Theoretical Semantics
Konstantin Sokolov
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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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Automatic Extraction of High-Quality Example Sentences for Word Learning Using a Determinantal Point Process
Arseny Tolmachev
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Sadao Kurohashi
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Retrieval Term Prediction Using Deep Belief Networks
Qing Ma
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Ibuki Tanigawa
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Masaki Murata
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Acquiring distributed representations for verb-object pairs by using word2vec
Miki Iwai
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Takashi Ninomiya
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Kyo Kageura
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http://www.cs.ox.ac.uk/
http://homepages.inf.ed.ac.uk/
http://wacky.sslmit.unibo.it/
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