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Utilizing Word Embeddings for Result Diversification in Tweet Search
Date
2015-12-04
Author
Onal, Kezban Dilek
Altıngövde, İsmail Sengör
Karagöz, Pınar
Metadata
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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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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.