Polarity Preference of Verbs: What Could Verbs Reveal about the Polarity of Their Objects?

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 dependencyparsed 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%. Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-64977 Originally published at: 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. Polarity preference of verbs: What could verbs reveal about the polarity of their objects? Manfred Klenner and Stefanos Petrakis Institute for Computational Linguistics, University of Zurich, Switzerland, {klenner,petrakis}@cl.uzh.ch, http://www.cl.uzh.ch/ 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 dependencyparsed 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%. 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 dependencyparsed 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%.