Word Sense and Subjectivity

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 1065–1072, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Word Sense and Subjectivity Janyce Wiebe Department of Computer Science University of Pittsburgh wiebe@cs.pitt.edu Rada Mihalcea Department of Computer Science University of North Texas rada@cs.unt.edu Abstract Subjectivity and meaning are both impor- tant properties of language. This paper ex- plores their interaction, and brings empir- ical evidence in support of the hypotheses that (1) subjectivity is a property that can be associated with word senses, and (2) word sense disambiguation can directly benefit from subjectivity annotations. 1 Introduction There is growing interest in the automatic extrac- tion of opinions, emotions, and sentiments in text (subjectivity), to provide tools and support for var- ious NLP applications. Similarly, there is continu- ous interest in the task of word sense disambigua- tion, with sense-annotated resources being devel- oped for many languages, and a growing num- ber of research groups participating in large-scale evaluations such as SENSEVAL. Though both of these areas are concerned with the semantics of a text, over time there has been little interaction, if any, between them. In this pa- per, we address this gap, and explore possible in- teractions between subjectivity and word sense. There are several benefits that would motivate such a joint exploration. First, at the resource level, the augmentation of lexical resources such as WordNet (Miller, 1995) with subjectivity labels could support better subjectivity analysis tools, and principled methods for refining word senses and clustering similar meanings. Second, at the tool level, an explicit link between subjectivity and word sense could help improve methods for each, by integrating features learned from one into the other in a pipeline approach, or through joint si- multaneous learning. In this paper we address two questions about word sense and subjectivity. First, can subjectiv- ity labels be assigned to word senses? To address this question, we perform two studies. The first (Section 3) investigates agreement between anno- tators who manually assign the labels subjective, objective, or both to WordNet senses. The second study (Section 4) evaluates a method for automatic assignment of subjectivity labels to word senses. We devise an algorithm relying on distributionally similar words to calculate a subjectivity score, and show how it can be used to automatically assess the subjectivity of a word sense. Second, can automatic subjectivity analysis be used to improve word sense disambiguation? To address this question, the output of a subjectivity sentence classifier is input to a word-sense disam- biguation system, which is in turn evaluated on the nouns from the SENSEVAL-3 English lexical sam- ple task (Section 5). The results of this experiment show that a subjectivity feature can significantly improve the accuracy of a word sense disambigua- tion system for those words that have both subjec- tive and objective senses. A third obvious question is, can word sense dis- ambiguation help automatic subjectivity analysis? However, due to space limitations, we do not ad- dress this …

Content truncated to 500 words