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Scaling to Large³ Data: An Efficient and Effective Method to Compute Distributional Thesauri
Martin Riedl
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Chris Biemann
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Paper Details:
Month: October
Year: 2013
Location: Seattle, Washington, USA
Venue:
EMNLP |
SIG: SIGDAT
Citations
URL
Combining Supervised and Unsupervised Parsing for Distributional Similarity
Martin Riedl
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Irina Alles
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Chris Biemann
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Detecting spelling variants in non-standard texts
Fabian Barteld
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DISTRIBUTED DISTRIBUTIONAL SIMILARITIES OF GOOGLE BOOKS OVER THE CENTURIES
Martin Riedl
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Richard Steuer
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Chris Biemann
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Network Features Based Co-hyponymy Detection
Abhik Jana
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Pawan Goyal
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Automatic Thesaurus Construction for Modern Hebrew
Chaya Liebeskind
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Ido Dagan
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Jonathan Schler
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Can Network Embedding of Distributional Thesaurus Be Combined with Word Vectors for Better Representation?
Abhik Jana
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Pawan Goyal
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That’s sick dude!: Automatic identification of word sense change across different timescales
Sunny Mitra
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Ritwik Mitra
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Martin Riedl
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Chris Biemann
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Animesh Mukherjee
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Pawan Goyal
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Probabilistic Modeling of Joint-context in Distributional Similarity
Oren Melamud
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Ido Dagan
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Jacob Goldberger
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Idan Szpektor
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Deniz Yuret
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Multiobjective Optimization and Unsupervised Lexical Acquisition for Named Entity Recognition and Classification
Govind
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Asif Ekbal
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Chris Biemann
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There’s no ‘Count or Predict’ but task-based selection for distributional models
Martin Riedl
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Chris Biemann
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https://sf.net/projects/jobimtext/
Field Of Study
Linguistic Trends
Distributional Semantics
Language
English
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