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Polarity preference of verbs: What could verbs reveal about the polarity of their objects?


Klenner, Manfred; Petrakis, Stefanos (2012). Polarity preference of verbs: What could verbs reveal about the polarity of their objects? In: Bouma, Gosse; Ittoo, Ashwin; Métais, Elisabeth; Wortmann, Hans. Natural Language Processing and Information Systems. 17th International Conference on Applications of Natural Language to Information Systems. Heidelberg: Springer, 35-46.

Abstract

The current endeavour focuses on the notion of positive versus negative polarity preference of verbs for their direct objects. This preference has to be distinguished from a verb's own prior polarity - for the same verb, these two properties might even be inverse. Polarity
preferences of verbs are extracted on the basis of a large and dependency- parsed corpus by means of statistical measures. We observed verbs with a relatively clear positive or negative polarity preference, as well as cases
of verbs where positive and negative polarity preference is balanced (we call these bipolar-preference verbs). Given clear-cut polarity preferences of a verb, nouns, whose polarity is yet unknown, can now be classified. We reached a lower bound of 81% precision in our experiments, whereas
the upper bound goes up to 92%.

Abstract

The current endeavour focuses on the notion of positive versus negative polarity preference of verbs for their direct objects. This preference has to be distinguished from a verb's own prior polarity - for the same verb, these two properties might even be inverse. Polarity
preferences of verbs are extracted on the basis of a large and dependency- parsed corpus by means of statistical measures. We observed verbs with a relatively clear positive or negative polarity preference, as well as cases
of verbs where positive and negative polarity preference is balanced (we call these bipolar-preference verbs). Given clear-cut polarity preferences of a verb, nouns, whose polarity is yet unknown, can now be classified. We reached a lower bound of 81% precision in our experiments, whereas
the upper bound goes up to 92%.

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Computational Linguistics
Dewey Decimal Classification:000 Computer science, knowledge & systems
410 Linguistics
Language:English
Date:2012
Deposited On:05 Oct 2012 06:30
Last Modified:26 Jan 2017 08:52
Publisher:Springer
Series Name:Lecture Notes in Computer Science
ISBN: 978-3-642-31177-2

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