Title | Discriminating Rhetorical Analogies in Social Media |
Publication Type | Conference Paper |
Year of Publication | 2014 |
Authors | Lofi, C., C. Nieke, and N. Collier |
Conference Name | 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL) |
Date Published | 04/2014 |
Conference Location | Gothenburg, Sweden |
Abstract | 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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Attachment | Size |
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14EACL_05_final.pdf | 761.24 KB |
crowd_sourcing_results_full.csv | 2.09 MB |
goldset_snippets.csv | 1.92 MB |