TY - JOUR
T1 - Evolutionary optimization for ranking how-to questions based on user-generated contents
AU - Atkinson, John
AU - Figueroa, Alejandro
AU - Andrade, Christian
N1 - Funding Information:
This research was partially supported by FONDECYT, Chile under Grant number 1130035 : “An Evolutionary Computation Approach to Natural-Language Chunking for Biological Text Mining Applications”.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Community question-answering
KW - Concept clustering
KW - Evolutionary computation
KW - HPSG parsing
KW - Question-answering systems
UR - http://www.scopus.com/inward/record.url?scp=84880584113&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2013.06.017
DO - 10.1016/j.eswa.2013.06.017
M3 - Article
AN - SCOPUS:84880584113
VL - 40
SP - 7060
EP - 7068
JO - Expert Systems with Applications
JF - Expert Systems with Applications
SN - 0957-4174
IS - 17
ER -