Multilingual Harvesting of Cross-Cultural Stereotypes

Proceedings of ACL-08: HLT, pages 523–531, Columbus, Ohio, USA, June 2008. c⃝2008 Association for Computational Linguistics Multilingual Harvesting of Cross-Cultural Stereotypes Tony Veale School of Computer Science University College Dublin Belfield, Dublin 4, Ireland tony.veale@ucd.ie Yanfen Hao School of Computer Science University College Dublin Belfield, Dublin 4, Ireland yanfen.hao@ucd.ie Guofu Li School of Computer Science University College Dublin Belfield, Dublin 4, Ireland li.guofu.l@gmail.com Abstract People rarely articulate explicitly what a na- tive speaker of a language is already assumed to know. So to acquire the stereotypical knowledge that underpins much of what is said in a given culture, one must look to what is implied by language rather than what is overtly stated. Similes are a convenient ve- hicle for this kind of knowledge, insofar as they mark out the most salient aspects of the most frequently evoked concepts. In this pa- per we perform a multilingual exploration of the space of common-place similes, by min- ing a large body of Chinese similes from the web and comparing these to the English sim- iles harvested by Veale and Hao (2007). We demonstrate that while the simile-frame is in- herently leaky in both languages, a multilin- gual analysis allows us to filter much of the noise that otherwise hinders the knowledge extraction process. In doing so, we can also identify a core set of stereotypical descrip- tions that exist in both languages and accu- rately map these descriptions onto a multilin- gual lexical ontology like HowNet. Finally, we demonstrate that conceptual descriptions that are derived from common-place similes are extremely compact and predictive of onto- logical structure. 1 Introduction Direct perception of our environment is just one of the ways we can acquire knowledge of the world. Another, more distinctly human approach, is through the comprehension of linguistic descrip- tions of another person’s perceptions and beliefs. Since computers have limited means of human-like perception, the latter approach is also very much suited to the automatic acquisition of world knowl- edge by a computer (see Hearst, 1992; Charniak and Berland, 1999; Etzioni et al., 2004; V¨olker et al., 2005; Almuhareb and Poesio, 2005; Cimiano and Wenderoth, 2007; Veale and Hao, 2007). Thus, by using the web as a distributed text corpus (see Keller et al., 2002), a multitude of facts and beliefs can be extracted, for purposes ranging from question- answering to ontology population. The possible configurations of different concepts can also be learned from how the words denoting these concepts are distributed; thus, a computer can learn that coffee is a beverage that can be served hot or cold, white or black, strong or weak and sweet or bitter (see Almuhareb and Poesio, 2005). But it is difficult to discern from these facts the idealized or stereotypical states of the world, e.g., that one ex- pects coffee to be hot and beer to be cold, so that if one spills coffee, we naturally infer the possibilities of scalding and staining without having to be told that the coffee was hot or …

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