Abstract
It has been found that very often long queries are more challenging than short queries for information search engines to obtain good results. In this paper, we present a word embedding-based approach. First short queries or concepts are extracted from the original query. Then with the help of a trained word embedding model, all of the query elements go through a series of reformulation operations including deletion, substitution, and addition of terms so as to obtain more profitable query representations. Finally all the reformulated elements are linearly combined with the original query. Experiments are conducted on three TREC collections, and the experimental results show that the proposed method is able to improve retrieval performance on average and especially effective for long queries. Compared with several state-of-the-art baseline methods, the proposed method is very good.
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Notes
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https://searchengineland.com/20-googles-limits-may-not-know-exist-281387, retrieved on 21 January, 2020.
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Yan, W., Wang, Y., Huang, C., Wu, S. (2020). Word Embedding-Based Reformulation for Long Queries in Information Search. In: Wang, G., Lin, X., Hendler, J., Song, W., Xu, Z., Liu, G. (eds) Web Information Systems and Applications. WISA 2020. Lecture Notes in Computer Science(), vol 12432. Springer, Cham. https://doi.org/10.1007/978-3-030-60029-7_19
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