TY - GEN
T1 - Text retrieval by term co-occurrences in a query-based vector space
AU - Sneiders, Eriks
N1 - Publisher Copyright:
© 1963-2018 ACL.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85042323402
M3 - Conference paper
AN - SCOPUS:85042323402
SN - 9784879747020
T3 - COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers
SP - 2356
EP - 2365
BT - COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016
PB - Association for Computational Linguistics, ACL Anthology
T2 - 26th International Conference on Computational Linguistics, COLING 2016
Y2 - 11 December 2016 through 16 December 2016
ER -