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 |
| _version_ | 1848761437821861888 |
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| author | Rattinger, A. Le Goff, J. Guetl, Christian |
| author_facet | Rattinger, A. Le Goff, J. Guetl, Christian |
| author_sort | Rattinger, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 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. |
| first_indexed | 2025-11-14T10:31:40Z |
| format | Conference Paper |
| id | curtin-20.500.11937-66959 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:31:40Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-669592018-05-18T07:56:44Z Local word embeddings for query expansion based on co-authorship and citations Rattinger, A. Le Goff, J. Guetl, Christian © 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. 2018 Conference Paper http://hdl.handle.net/20.500.11937/66959 restricted |
| spellingShingle | Rattinger, A. Le Goff, J. Guetl, Christian Local word embeddings for query expansion based on co-authorship and citations |
| title | Local word embeddings for query expansion based on co-authorship and citations |
| title_full | Local word embeddings for query expansion based on co-authorship and citations |
| title_fullStr | Local word embeddings for query expansion based on co-authorship and citations |
| title_full_unstemmed | Local word embeddings for query expansion based on co-authorship and citations |
| title_short | Local word embeddings for query expansion based on co-authorship and citations |
| title_sort | local word embeddings for query expansion based on co-authorship and citations |
| url | http://hdl.handle.net/20.500.11937/66959 |