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Ranking Relevant Verb Phrases Extracted from Historical Text
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology. (Computational Linguistics)
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology. (Computational Linguistics)ORCID iD: 0000-0002-4838-6518
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology. (Computational Linguistics)
2015 (English)In: Proceedings of the 9th SIGHUM Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, 2015Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we present three approaches to automatic ranking of relevant verb phrases extracted from historical text. These approaches are based on conditional probability, log likelihood ratio, and bagof-words classification respectively. The aim of the ranking in our study is to present verb phrases that have a high probability of describing work at the top of the results list, but the methods are likely to be applicable to other information needs as well. The results are evaluated by use of three different evaluation metrics: precision at k, R-precision, and average precision. In the best setting, 91 out of the top-100 instances in the list are true positives.

Place, publisher, year, edition, pages
2015.
National Category
Language Technology (Computational Linguistics)
Research subject
Computational Linguistics
Identifiers
URN: urn:nbn:se:uu:diva-264780OAI: oai:DiVA.org:uu-264780DiVA: diva2:861518
Conference
ACL 2015
Available from: 2015-10-17 Created: 2015-10-17 Last updated: 2017-01-25

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Pettersson, EvaMegyesi, Beata

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