A new Twitter verb lexicon for natural language processing.
A new Twitter verb lexicon for natural language processing.
We describe in-progress work on the creation of a new lexical resource that contains a list of 486 verbs annotated with quantified temporal durations for the events that they describe. This resource is being compiled from more than 14 million tweets from the Twitter microblogging site. We are creating this lexicon of verbs and typical durations to address a gap in the available information that is represented in existing research. The data that is contained in this lexicon is unlike any existing resources, which have been traditionally comprised of literature excerpts, news stories, and full-length weblogs. This kind of knowledge about how long an event lasts is crucial for natural language processing and is especially useful when the temporal duration of an event is implied. We are using data from Twitter because Twitter is a rich resource since people are publicly posting real events and real durations of those events throughout the day.
European Language Resources Association
Williams, Jennifer
3a1568b4-8a0b-41d2-8635-14fe69fbb360
Katz, Garaham
dcae091a-0290-4bf5-886b-a2815ccc6177
25 May 2012
Williams, Jennifer
3a1568b4-8a0b-41d2-8635-14fe69fbb360
Katz, Garaham
dcae091a-0290-4bf5-886b-a2815ccc6177
Williams, Jennifer and Katz, Garaham
(2012)
A new Twitter verb lexicon for natural language processing.
In Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12).
European Language Resources Association.
6 pp
.
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Conference or Workshop Item
(Paper)
Abstract
We describe in-progress work on the creation of a new lexical resource that contains a list of 486 verbs annotated with quantified temporal durations for the events that they describe. This resource is being compiled from more than 14 million tweets from the Twitter microblogging site. We are creating this lexicon of verbs and typical durations to address a gap in the available information that is represented in existing research. The data that is contained in this lexicon is unlike any existing resources, which have been traditionally comprised of literature excerpts, news stories, and full-length weblogs. This kind of knowledge about how long an event lasts is crucial for natural language processing and is especially useful when the temporal duration of an event is implied. We are using data from Twitter because Twitter is a rich resource since people are publicly posting real events and real durations of those events throughout the day.
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Published date: 25 May 2012
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Local EPrints ID: 470365
URI: http://eprints.soton.ac.uk/id/eprint/470365
PURE UUID: 1495e52c-19f4-4623-8153-e8d2bc51da3b
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Date deposited: 07 Oct 2022 16:32
Last modified: 17 Mar 2024 04:12
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Author:
Jennifer Williams
Author:
Garaham Katz
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