Local word embeddings for query expansion based on co-authorship and citations
© Copyright 2018 for the individual papers by the papers' authors. Word embedding techniques have gained a lot of interest from natural language processing researchers recently and they are valuable resource in identifying a list of semantically related terms for a search query. These related t...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/66959 |
| Summary: | © Copyright 2018 for the individual papers by the papers' authors. Word embedding techniques have gained a lot of interest from natural language processing researchers recently and they are valuable resource in identifying a list of semantically related terms for a search query. These related terms build a natural addition for query expansion, but might mismatch when the application domains use different jargon. Using the Skip-Gram algorithm of Word2Vec, terms are selected only from a specific subset of the corpus, which is extended by documents from co-authorship and citations. We demonstrate that locally-trained word embeddings with this extension provides a valuable augmentation and can improve retrieval performance. First result suggest that query expansion and word embeddings could also benefit from other related information. |
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