@inproceedings{preotiuc-pietro-etal-2016-studying,
title = "Studying the Temporal Dynamics of Word Co-occurrences: An Application to Event Detection",
author = "Preo{\c{t}}iuc-Pietro, Daniel and
Srijith, P. K. and
Hepple, Mark and
Cohn, Trevor",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Goggi, Sara and
Grobelnik, Marko and
Maegaard, Bente and
Mariani, Joseph and
Mazo, Helene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro{\v{z}}, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L16-1694",
pages = "4380--4387",
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.",
}
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%0 Conference Proceedings
%T Studying the Temporal Dynamics of Word Co-occurrences: An Application to Event Detection
%A Preoţiuc-Pietro, Daniel
%A Srijith, P. K.
%A Hepple, Mark
%A Cohn, Trevor
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Grobelnik, Marko
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Helene
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 May
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F preotiuc-pietro-etal-2016-studying
%X 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.
%U https://aclanthology.org/L16-1694
%P 4380-4387
Markdown (Informal)
[Studying the Temporal Dynamics of Word Co-occurrences: An Application to Event Detection](https://aclanthology.org/L16-1694) (Preoţiuc-Pietro et al., LREC 2016)
ACL