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.
| Original language | English |
|---|---|
| Pages (from-to) | 7060-7068 |
| Number of pages | 9 |
| Journal | Expert Systems with Applications |
| Volume | 40 |
| Issue number | 17 |
| DOIs | |
| State | Published - 2013 |
| Externally published | Yes |
Keywords
- Community question-answering
- Concept clustering
- Evolutionary computation
- HPSG parsing
- Question-answering systems