Making Sense of Word Embeddings
Autor: | Chris Biemann, Nikolay Arefiev, Maria Pelevina, Alexander Panchenko |
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Jazyk: | angličtina |
Rok vydání: | 2017 |
Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language Computer science business.industry Contrast (statistics) Context (language use) 02 engineering and technology Sense (electronics) computer.software_genre Word sense Simple (abstract algebra) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Cluster analysis business Computation and Language (cs.CL) computer Natural language processing Word (computer architecture) |
Zdroj: | Rep4NLP@ACL |
Popis: | We present a simple yet effective approach for learning word sense embeddings. In contrast to existing techniques, which either directly learn sense representations from corpora or rely on sense inventories from lexical resources, our approach can induce a sense inventory from existing word embeddings via clustering of ego-networks of related words. An integrated WSD mechanism enables labeling of words in context with learned sense vectors, which gives rise to downstream applications. Experiments show that the performance of our method is comparable to state-of-the-art unsupervised WSD systems. |
Databáze: | OpenAIRE |
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