Utilizing Word Embeddings for Result Diversification in Tweet Search

Date

2015-12-04

Author

Onal, Kezban Dilek
Altıngövde, İsmail Sengör
Karagöz, Pınar

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The performance of result diversification for tweet search suffers from the well-known vocabulary mismatch problem, as tweets are too short and usually informal. As a remedy, we propose to adopt a query and tweet expansion strategy that utilizes automatically-generated word embeddings. Our experiments using state-of-the-art diversification methods on the Tweets2013 corpus reveal encouraging results for expanding queries and/or tweets based on the word embeddings to improve the diversification performance in tweet search. We further show that the expansions based on the word embeddings may serve as useful as those based on a manually constructed knowledge base, namely, ConceptNet.

Subject Keywords

Diversification, Expansion, Tweet search, Word embedding

URI

https://hdl.handle.net/11511/38301

DOI

https://doi.org/10.1007/978-3-319-28940-3_29

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

K. D. Onal, İ. S. Altıngövde, and P. Karagöz, “Utilizing Word Embeddings for Result Diversification in Tweet Search,” 2015, vol. 9460, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/38301.