Word vector compositionality based relevance feedback using kernel density estimation
Document Type
Conference Article
Publication Title
International Conference on Information and Knowledge Management, Proceedings
Abstract
A limitation of standard information retrieval (IR) models is that the notion of term composionality is restricted to predefined phrases and term proximity. Standard text based IR models provide no easy way of representing semantic relations between terms that are not necessarily phrases, such as the equivalence relationship between 'osteoporosis' and the terms 'bone' and 'decay'. To alleviate this limitation, we introduce a relevance feedback (RF) method which makes use of word embedded vectors. We leverage the fact that the vector addition of word embeddings leads to a semantic composition of the corresponding terms, e.g. addition of the vectors for 'bone' and 'decay' yields a vector that is likely to be close to the vector for the word 'osteoporosis'. Our proposed RF model enables incorporation of semantic relations by exploiting term compositionality with embedded word vectors. We develop our model for RF as a generalization of the relevance model (RLM). Our experiments demonstrate that our word embedding based RF model significantly outperforms the RLM model on standard TREC test collections, namely the TREC 6,7,8 and Robust ad-hoc and the TREC 9 and 10 WT10G test collections.
First Page
1281
Last Page
1290
DOI
10.1145/2983323.2983750
Publication Date
10-24-2016
Recommended Citation
Roy, Dwaipayan; Ganguly, Debasis; Mitra, Mandar; and Jones, Gareth J.F., "Word vector compositionality based relevance feedback using kernel density estimation" (2016). Conference Articles. 815.
https://digitalcommons.isical.ac.in/conf-articles/815
