Text Retrieval by Term Co-occurrences in a Query-based Vector Space

Eriks Sneiders


Abstract
Term co-occurrence in a sentence or paragraph is a powerful and often overlooked feature for text matching in document retrieval. In our experiments with matching email-style query messages to webpages, such term co-occurrence helped greatly to filter and rank documents, compared to matching document-size bags-of-words. The paper presents the results of the experiments as well as a text-matching model where the query shapes the vector space, a document is modelled by two or three vectors in this vector space, and the query-document similarity score depends on the length of the vectors and the relationships between them.
Anthology ID:
C16-1222
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Yuji Matsumoto, Rashmi Prasad
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
2356–2365
Language:
URL:
https://aclanthology.org/C16-1222
DOI:
Bibkey:
Cite (ACL):
Eriks Sneiders. 2016. Text Retrieval by Term Co-occurrences in a Query-based Vector Space. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2356–2365, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Text Retrieval by Term Co-occurrences in a Query-based Vector Space (Sneiders, COLING 2016)
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PDF:
https://aclanthology.org/C16-1222.pdf