Chinese Answer Extraction Based on POS Tree and Genetic Algorithm

Shuihua Li, Xiaoming Zhang, Zhoujun Li


Abstract
Answer extraction is the most important part of a chinese web-based question answering system. In order to enhance the robustness and adaptability of answer extraction to new domains and eliminate the influence of the incomplete and noisy search snippets, we propose two new answer exraction methods. We utilize text patterns to generate Part-of-Speech (POS) patterns. In addition, a method is proposed to construct a POS tree by using these POS patterns. The POS tree is useful to candidate answer extraction of web-based question answering. To retrieve a efficient POS tree, the similarities between questions are used to select the question-answer pairs whose questions are similar to the unanswered question. Then, the POS tree is improved based on these question-answer pairs. In order to rank these candidate answers, the weights of the leaf nodes of the POS tree are calculated using a heuristic method. Moreover, the Genetic Algorithm (GA) is used to train the weights. The experimental results of 10-fold crossvalidation show that the weighted POS tree trained by GA can improve the accuracy of answer extraction.
Anthology ID:
W17-6004
Volume:
Proceedings of the 9th SIGHAN Workshop on Chinese Language Processing
Month:
December
Year:
2017
Address:
Taiwan
Venues:
SIGHAN | WS
SIG:
SIGHAN
Publisher:
Association for Computational Linguistics
Note:
Pages:
30–36
Language:
URL:
https://aclanthology.org/W17-6004
DOI:
Bibkey:
Cite (ACL):
Shuihua Li, Xiaoming Zhang, and Zhoujun Li. 2017. Chinese Answer Extraction Based on POS Tree and Genetic Algorithm. In Proceedings of the 9th SIGHAN Workshop on Chinese Language Processing, pages 30–36, Taiwan. Association for Computational Linguistics.
Cite (Informal):
Chinese Answer Extraction Based on POS Tree and Genetic Algorithm (Li et al., 2017)
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PDF:
https://aclanthology.org/W17-6004.pdf