Evolutionary optimization for ranking how-to questions based on user-generated contents

John Atkinson, Alejandro Figueroa, Christian Andrade

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageEnglish
Pages (from-to)7060-7068
Number of pages9
JournalExpert Systems with Applications
Issue number17
StatePublished - 2013
Externally publishedYes


  • Community question-answering
  • Concept clustering
  • Evolutionary computation
  • HPSG parsing
  • Question-answering systems


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