Wikification of Concept Mentions within Spoken Dialogues Using Domain Constraints from Wikipedia

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2225–2229, Lisbon, Portugal, 17-21 September 2015. c⃝2015 Association for Computational Linguistics. Wikification of Concept Mentions within Spoken Dialogues Using Domain Constraints from Wikipedia Seokhwan Kim, Rafael E. Banchs, Haizhou Li Human Language Technology Department Institute for Infocomm Research Singapore 138632 {kims,rembanchs,hli}@i2r.a-star.edu.sg Abstract While most previous work on Wikification has focused on written texts, this paper presents a Wikification approach for spo- ken dialogues. A set of analyzers are pro- posed to learn dialogue-specific properties along with domain knowledge of conver- sations from Wikipedia. Then, the an- alyzed properties are used as constraints for generating candidates, and the candi- dates are ranked to find the appropriate links. The experimental results show that our proposed approach can significantly improve the performances of the task in human-human dialogues. 1 Introduction Linking mentions in natural language to the relevant concepts in knowledge-bases plays a key role in better understanding the meanings of expressions as well as further populating knowledge-bases with less human effort. Espe- cially, Wikipedia has been widely used as a major target resource for linking. Most previous work on this Wikipedia-based linking task called Wiki- fication (Mihalcea and Csomai, 2007) has focused on resolving ambiguities and variabilities of the expressions in written texts including newswire collections (McNamee and Dang, 2009; Ji et al., 2010; Ji et al., 2014) or microblog posts (Genc et al., 2011; Cassidy et al., 2012; Guo et al., 2013; Huang et al., 2014). But writing and reading are not the only ways for exchange of information, since many commu- nications between people in real life are performed through spoken dialogues also. Thus, we could expect to improve the understanding capabilities of applications based on Wikification and broaden the coverage of the contents in knowledge-bases, if Wikification is successfully performed also for human-human spoken conversations. In this work, we focus on the following differ- ences between spoken dialogues and written texts as sources for Wikification. Firstly, at least two speakers are engaged in a dialogue session, while the texts in newswire or microblogs are mostly written by a single author. Thus, the viewpoint of each speaker should be considered separately or jointly depending on the situation. Secondly, the correspondence between mentions and concepts in spoken dialogues tends to be dependent not only on the contexts explicitly mentioned in a given di- alogue, but also on other information inferred by speakers based on their background knowledge. The other difference is that spoken utterances are more likely to be informal and noisy than writ- ten sentences, which makes expressions more am- biguous and variable. To solve these issues, we propose a three step approach for Wikification on spoken dialogues. At the first step, a set of classifiers are used for an- alyzing the dialogue-specific aspects of a given mention. According to the analyzed results, the criteria in selecting concept candidates is deter- mined, and then a ranking is performed on the fil- tered candidates to identify the concept …

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