Graph-based Event Extraction from Twitter

Abstract : Event detection on Twitter has become an attractive and challenging research field due to the popularity and the peculiarities of tweets. 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.
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Communication dans un congrès
RANLP17 - Recent advances in natural language processing, Jul 2017, Varna, Bulgaria. 2017
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Soumis le : mercredi 19 juillet 2017 - 07:37:45
Dernière modification le : mercredi 13 décembre 2017 - 10:16:05

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Amosse Edouard, Elena Cabrio, Sara Tonelli, Nhan Le Thanh. Graph-based Event Extraction from Twitter. RANLP17 - Recent advances in natural language processing, Jul 2017, Varna, Bulgaria. 2017. 〈hal-01561439〉

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