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Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors
Marco Baroni
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Georgiana Dinu
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Germán Kruszewski
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Paper Details:
Month: June
Year: 2014
Location: Baltimore, Maryland
Venue:
ACL |
Citations
URL
Evaluating Neural Word Representations in Tensor-Based Compositional Settings
Dmitrijs Milajevs
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Dimitri Kartsaklis
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Mehrnoosh Sadrzadeh
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Matthew Purver
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The Inside-Outside Recursive Neural Network model for Dependency Parsing
Phong Le
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Willem Zuidema
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Using Mined Coreference Chains as a Resource for a Semantic Task
Heike Adel
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Hinrich Schütze
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Glove: Global Vectors for Word Representation
Jeffrey Pennington
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Richard Socher
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Christopher Manning
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Applying Multi-Sense Embeddings for German Verbs to Determine Semantic Relatedness and to Detect Non-Literal Language
Maximilian Köper
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Sabine Schulte im Walde
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How to make words with vectors: Phrase generation in distributional semantics
Georgiana Dinu
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Marco Baroni
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Linguistic Regularities in Sparse and Explicit Word Representations
Omer Levy
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Yoav Goldberg
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Exploring the use of word embeddings and random walks on Wikipedia for the CogAlex shared task
Josu Goikoetxea
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Eneko Agirre
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Aitor Soroa
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Complex Verbs are Different: Exploring the Visual Modality in Multi-Modal Models to Predict Compositionality
Maximilian Köper
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Sabine Schulte im Walde
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http://homepages.inf.ed.ac.uk/
http://wacky.sslmit.unibo.it
http://en.wikipedia.org
http://www.natcorp.ox.ac.uk
http://clic.cimec.unitn.it/composes/
https://code.google.com/p/word2vec/
http://clic.cimec.unitn.it/dm/
http://ronan.collobert.com/senna/
http://bit.ly/
http://clic.cimec.unitn.it/
https://sites.google.com/site/
http://www.cl.cam.ac.uk/
http://arxiv.org/
http://arxiv.org/abs/
Field Of Study
Linguistic Trends
Lexical Semantics
Embeddings
Syntax
Task
Semantic Similarity
Approach
Deep Learning
Semi-supervised Learning
Language
English
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