Short texts, due to their nature which makes them full of abbreviations and new coined acronyms, are not easy to classify. Text enrichment is emerging in the literature as a potentially useful tool. This paper is a part of a longer term research that aims at understanding the effectiveness of tweet enrichment by means of news, instead of the whole web as a knowledge source. Since the choice of a news collection may contribute to produce very different outcomes in the enrichment process, we compare the impact of three features of such collections: volume, variety, and freshness. We show that all three features have a significant impact on categorization accuracy. Copyright © 2016 for the individual papers by the paper's authors.

Exploiting news to categorize tweets: Quantifying the impact of different news collections

PAVAN, Marco;MIZZARO, Stefano;SCAGNETTO, Ivan
2016

Abstract

Short texts, due to their nature which makes them full of abbreviations and new coined acronyms, are not easy to classify. Text enrichment is emerging in the literature as a potentially useful tool. This paper is a part of a longer term research that aims at understanding the effectiveness of tweet enrichment by means of news, instead of the whole web as a knowledge source. Since the choice of a news collection may contribute to produce very different outcomes in the enrichment process, we compare the impact of three features of such collections: volume, variety, and freshness. We show that all three features have a significant impact on categorization accuracy. Copyright © 2016 for the individual papers by the paper's authors.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11390/1091779
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