Graph-based Event Extraction from Twitter

Amosse Edouard, Elena Cabrio, Sara Tonelli, Nhan Le-Thanh


Abstract
Detecting which tweets describe a specific event and clustering them is one of the main challenging tasks related to Social Media currently addressed in the NLP community. Existing approaches have mainly focused on detecting spikes in clusters around specific keywords or Named Entities (NE). However, one of the main drawbacks of such approaches is the difficulty in understanding when the same keywords describe different events. In this paper, we propose a novel approach that exploits NE mentions in tweets and their entity context to create a temporal event graph. Then, using simple graph theory techniques and a PageRank-like algorithm, we process the event graphs to detect clusters of tweets describing the same events. Experiments on two gold standard datasets show that our approach achieves state-of-the-art results both in terms of evaluation performances and the quality of the detected events.
Anthology ID:
R17-1031
Volume:
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
Month:
September
Year:
2017
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
222–230
Language:
URL:
https://doi.org/10.26615/978-954-452-049-6_031
DOI:
10.26615/978-954-452-049-6_031
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PDF:
https://doi.org/10.26615/978-954-452-049-6_031