<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="li-etal-2017-chinese">
    <titleInfo>
        <title>Chinese Answer Extraction Based on POS Tree and Genetic Algorithm</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Shuihua</namePart>
        <namePart type="family">Li</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xiaoming</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zhoujun</namePart>
        <namePart type="family">Li</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 9th SIGHAN Workshop on Chinese Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Yue</namePart>
            <namePart type="family">Zhang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Zhifang</namePart>
            <namePart type="family">Sui</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Taiwan</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <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.</abstract>
    <identifier type="citekey">li-etal-2017-chinese</identifier>
    <location>
        <url>https://aclanthology.org/W17-6004</url>
    </location>
    <part>
        <date>2017-12</date>
        <extent unit="page">
            <start>30</start>
            <end>36</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Chinese Answer Extraction Based on POS Tree and Genetic Algorithm
%A Li, Shuihua
%A Zhang, Xiaoming
%A Li, Zhoujun
%Y Zhang, Yue
%Y Sui, Zhifang
%S Proceedings of the 9th SIGHAN Workshop on Chinese Language Processing
%D 2017
%8 December
%I Association for Computational Linguistics
%C Taiwan
%F li-etal-2017-chinese
%X 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.
%U https://aclanthology.org/W17-6004
%P 30-36

Markdown (Informal)

[Chinese Answer Extraction Based on POS Tree and Genetic Algorithm](https://aclanthology.org/W17-6004) (Li et al., SIGHAN 2017)

• Chinese Answer Extraction Based on POS Tree and Genetic Algorithm (Li et al., SIGHAN 2017)

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.