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LexSemTm: A Semantic Dataset Based on All-words Unsupervised Sense Distribution Learning
Andrew Bennett
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Timothy Baldwin
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Jey Han Lau
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Diana McCarthy
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Francis Bond
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Paper Details:
Month: August
Year: 2016
Location: Berlin, Germany
Venue:
ACL |
Citations
URL
Train-O-Matic: Large-Scale Supervised Word Sense Disambiguation in Multiple Languages without Manual Training Data
Tommaso Pasini
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Roberto Navigli
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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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https://github.com/awbennett/LexSemTm
http://www.mloss.org/software/view/527
Field Of Study
Linguistic Trends
Discourse
Embeddings
Task
Word Sense Disambiguation
Information Extraction
Information Retrieval
Approach
Topic Modeling
Language
Chinese
English
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