Evolutionary optimization for ranking how-to questions based on user-generated contents |
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Authors: | John Atkinson Alejandro Figueroa Christian Andrade |
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Affiliation: | 1. Department of Computer Sciences, Faculty of Engineering, Universidad de Concepcion, Concepcion, Chile;2. Yahoo! Research Latin America, Av. Blanco Encalada 2120, Santiago, Chile |
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Abstract: | In this work, a new evolutionary model is proposed for ranking answers to non-factoid (how-to) questions in community question-answering platforms. The approach combines evolutionary computation techniques and clustering methods to effectively rate best answers from web-based user-generated contents, so as to generate new rankings of answers. Discovered clusters contain semantically related triplets representing question–answers pairs in terms of subject-verb-object, which is hypothesized to improve the ranking of candidate answers. Experiments were conducted using our evolutionary model and concept clustering operating on large-scale data extracted from Yahoo! Answers. Results show the promise of the approach to effectively discovering semantically similar questions and improving the ranking as compared to state-of-the-art methods. |
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Keywords: | Community question-answering Question-answering systems Concept clustering Evolutionary computation HPSG parsing |
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