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LSTMEmbed: Learning Word and Sense Representations from a Large Semantically Annotated Corpus with Long Short-Term Memories
Ignacio Iacobacci
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Roberto Navigli
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Paper Details:
Month: July
Year: 2019
Location: Florence, Italy
Venue:
ACL |
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URL
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http://lcl.uniroma1
https://babelnet.org
http://developers.google.com/freebase
http://lcl.uniroma1.it/sensembed/
http://babelfy.org
http://lcl.uniroma1.it/
https://keras.io
http://deeplearning.net/software/
https://code.google.com/archive/p/
https://nlp.stanford.edu/projects/
Field Of Study
Linguistic Trends
Embeddings
Task
Language Understanding
Word Sense Disambiguation
Semantic Similarity
Machine Translation
Approach
Deep Learning
Representation Learning
Language
Multilingual
Chinese
English
Dataset
News