Discriminating Rhetorical Analogies in Social Media

TitleDiscriminating Rhetorical Analogies in Social Media
Publication TypeConference Paper
Year of Publication2014
AuthorsLofi, C., C. Nieke, and N. Collier
Conference Name 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL)
Date Published04/2014
Conference LocationGothenburg, Sweden

Analogies are considered to be one of the core concepts of human cognition and communication, and are very efficient at encoding complex information in a natural fashion. However, computational approaches towards large-scale analysis of the semantics of analogies are hampered by the lack of suitable corpora with real-life example of analogies. In this paper we therefore propose a workflow for discriminating and extracting natural-language analogy statements from the Web, focusing on analogies between locations mined from travel reports, blogs, and the Social Web. For realizing this goal, we employ feature-rich supervised learning models which we extensively evaluate. We also showcase a crowd-supported workflow for building a suitable Gold dataset used for this purpose. The resulting system is able to successfully learn to identify analogies to a high degree of accuracy (F-Score 0.9) by using a high-dimensional subsequence feature space.



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14EACL_05_final.pdf761.24 KB
crowd_sourcing_results_full.csv2.09 MB
goldset_snippets.csv1.92 MB