NEED4Tweet: A Twitterbot for Tweets Named Entity Extraction and Disambiguation

M. B. Habib; M. van Keulen

doi:10.3115/V1/P15-4006

NEED4Tweet: A Twitterbot for Tweets Named Entity Extraction and Disambiguation

M. B. Habib, M. van Keulen

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

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Abstract

In this demo paper, we present NEED4Tweet, a Twitterbot for named entity extraction (NEE) and disambiguation (NED) for Tweets. The straightforward application of state-of-the-art extraction and disambiguation approaches on informal text widely used in Tweets, typically results in significantly degraded performance due to the lack of formal structure; the lack of sufficient context required; and the seldom entities involved. In this paper, we introduce a novel framework that copes with the introduced challenges. We rely on contextual and semantic features more than syntactic features which are less informative. We believe that disambiguation can help to improve the extraction process. This mimics the way humans understand language.

Original language	English
Title of host publication	Proceedings of the System Demonstrations of The 53rd Annual Meeting of the Association for Computational Linguistics (ACL 2015), Beijing, China
Place of Publication	Beijing, China
Publisher	The Association for Computer Linguistics
DOIs	https://doi.org/10.3115/V1/P15-4006
Publication status	Published - 1 Jul 2015
Externally published	Yes

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10.3115/V1/P15-4006Licence: CC BY

Cite this

@inproceedings{42720c8966b140ecaf802faed8a01b65,

title = "NEED4Tweet: A Twitterbot for Tweets Named Entity Extraction and Disambiguation",

abstract = "In this demo paper, we present NEED4Tweet, a Twitterbot for named entity extraction (NEE) and disambiguation (NED) for Tweets. The straightforward application of state-of-the-art extraction and disambiguation approaches on informal text widely used in Tweets, typically results in significantly degraded performance due to the lack of formal structure; the lack of sufficient context required; and the seldom entities involved. In this paper, we introduce a novel framework that copes with the introduced challenges. We rely on contextual and semantic features more than syntactic features which are less informative. We believe that disambiguation can help to improve the extraction process. This mimics the way humans understand language.",

author = "Habib, {M. B.} and Keulen, {M. van}",

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doi = "10.3115/V1/P15-4006",

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booktitle = "Proceedings of the System Demonstrations of The 53rd Annual Meeting of the Association for Computational Linguistics (ACL 2015), Beijing, China",

publisher = "The Association for Computer Linguistics",

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NEED4Tweet: A Twitterbot for Tweets Named Entity Extraction and Disambiguation. / Habib, M. B.; Keulen, M. van.
Proceedings of the System Demonstrations of The 53rd Annual Meeting of the Association for Computational Linguistics (ACL 2015), Beijing, China. Beijing, China: The Association for Computer Linguistics, 2015.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

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