Identifying Topics by Position: Difference between revisions
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* '''Source''': SIGIR 2005: Proceedings of the 5<sup>th</sup> Conference on Applied Natural Language Processing | * '''Source''': SIGIR 2005: Proceedings of the 5<sup>th</sup> Conference on Applied Natural Language Processing | ||
* '''Pages''': 283-290 | * '''Pages''': 283-290 | ||
* '''Year''': | * '''Year''': 1997 | ||
* '''Authors''': Chin-Yew Lin and Eduard Hovy | * '''Authors''': Chin-Yew Lin and Eduard Hovy | ||
* '''Publisher''': ACL | * '''Publisher''': ACL | ||
Revision as of 16:58, 3 March 2009
Bibliographical data
- Source: SIGIR 2005: Proceedings of the 5th Conference on Applied Natural Language Processing
- Pages: 283-290
- Year: 1997
- Authors: Chin-Yew Lin and Eduard Hovy
- Publisher: ACL
Abstract
This paper addresses the problem of identifying likely topics of texts by their position in the text. It describes the automated training and evaluation of an Optimal Position Policy, a method of locating the likely positions of topic-bearing sentences based on genre-specific regularities of discourse structure. This method can be used in applications such as information retrieval, routing, and text summarization.