Discriminating Rhetorical Analogies in Social Media
Christoph Lofi, Christian Nieke, Nigel Collier · 2014
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 largescale 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.