@inproceedings{edouard-etal-2017-graph,
title = "Graph-based Event Extraction from {T}witter",
author = "Edouard, Amosse and
Cabrio, Elena and
Tonelli, Sara and
Le-Thanh, Nhan",
booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
month = sep,
year = "2017",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd.",
url = "https://doi.org/10.26615/978-954-452-049-6_031",
doi = "10.26615/978-954-452-049-6_031",
pages = "222--230",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Graph-based Event Extraction from Twitter
%A Edouard, Amosse
%A Cabrio, Elena
%A Tonelli, Sara
%A Le-Thanh, Nhan
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 sep
%I INCOMA Ltd.
%C Varna, Bulgaria
%F edouard-etal-2017-graph
%X 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.
%R 10.26615/978-954-452-049-6_031
%U https://doi.org/10.26615/978-954-452-049-6_031
%P 222-230
Markdown (Informal)
[Graph-based Event Extraction from Twitter](https://doi.org/10.26615/978-954-452-049-6_031) (Edouard et al., RANLP 2017)
ACL
- Amosse Edouard, Elena Cabrio, Sara Tonelli, and Nhan Le-Thanh. 2017. Graph-based Event Extraction from Twitter. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 222–230, Varna, Bulgaria. INCOMA Ltd..