Studying the Temporal Dynamics of Word Co-occurrences: An Application to Event Detection

Daniel Preoţiuc-Pietro, P. K. Srijith, Mark Hepple, Trevor Cohn


Abstract
Streaming media provides a number of unique challenges for computational linguistics. This paper studies the temporal variation in word co-occurrence statistics, with application to event detection. We develop a spectral clustering approach to find groups of mutually informative terms occurring in discrete time frames. Experiments on large datasets of tweets show that these groups identify key real world events as they occur in time, despite no explicit supervision. The performance of our method rivals state-of-the-art methods for event detection on F-score, obtaining higher recall at the expense of precision.
Anthology ID:
L16-1694
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
4380–4387
Language:
URL:
https://aclanthology.org/L16-1694
DOI:
Bibkey:
Cite (ACL):
Daniel Preoţiuc-Pietro, P. K. Srijith, Mark Hepple, and Trevor Cohn. 2016. Studying the Temporal Dynamics of Word Co-occurrences: An Application to Event Detection. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 4380–4387, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
Studying the Temporal Dynamics of Word Co-occurrences: An Application to Event Detection (Preoţiuc-Pietro et al., LREC 2016)
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PDF:
https://aclanthology.org/L16-1694.pdf