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        <title>A Two-Phase Approach Towards Identifying Argument Structure in Natural Language</title>
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    <name type="personal">
        <namePart type="given">Arkanath</namePart>
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    <name type="personal">
        <namePart type="given">Pawan</namePart>
        <namePart type="family">Goyal</namePart>
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    <name type="personal">
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            <title>Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)</title>
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        <name type="personal">
            <namePart type="given">Hsin-Hsi</namePart>
            <namePart type="family">Chen</namePart>
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            <namePart type="given">Yuen-Hsien</namePart>
            <namePart type="family">Tseng</namePart>
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            <namePart type="given">Xiaofei</namePart>
            <namePart type="family">Lu</namePart>
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    <abstract>We propose a new approach for extracting argument structure from natural language texts that contain an underlying argument. Our approach comprises of two phases: Score Assignment and Structure Prediction. The Score Assignment phase trains models to classify relations between argument units (Support, Attack or Neutral). To that end, different training strategies have been explored. We identify different linguistic and lexical features for training the classifiers. Through ablation study, we observe that our novel use of word-embedding features is most effective for this task. The Structure Prediction phase makes use of the scores from the Score Assignment phase to arrive at the optimal structure. We perform experiments on three argumentation datasets, namely, AraucariaDB, Debatepedia and Wikipedia. We also propose two baselines and observe that the proposed approach outperforms baseline systems for the final task of Structure Prediction.</abstract>
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        <date>2016-12</date>
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            <start>11</start>
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%0 Conference Proceedings
%T A Two-Phase Approach Towards Identifying Argument Structure in Natural Language
%A Pathak, Arkanath
%A Goyal, Pawan
%A Bhowmick, Plaban
%Y Chen, Hsin-Hsi
%Y Tseng, Yuen-Hsien
%Y Ng, Vincent
%Y Lu, Xiaofei
%S Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F pathak-etal-2016-two
%X We propose a new approach for extracting argument structure from natural language texts that contain an underlying argument. Our approach comprises of two phases: Score Assignment and Structure Prediction. The Score Assignment phase trains models to classify relations between argument units (Support, Attack or Neutral). To that end, different training strategies have been explored. We identify different linguistic and lexical features for training the classifiers. Through ablation study, we observe that our novel use of word-embedding features is most effective for this task. The Structure Prediction phase makes use of the scores from the Score Assignment phase to arrive at the optimal structure. We perform experiments on three argumentation datasets, namely, AraucariaDB, Debatepedia and Wikipedia. We also propose two baselines and observe that the proposed approach outperforms baseline systems for the final task of Structure Prediction.
%U https://aclanthology.org/W16-4903
%P 11-19

Markdown (Informal)

[A Two-Phase Approach Towards Identifying Argument Structure in Natural Language](https://aclanthology.org/W16-4903) (Pathak et al., NLP-TEA 2016)

• A Two-Phase Approach Towards Identifying Argument Structure in Natural Language (Pathak et al., NLP-TEA 2016)

ACL

• Arkanath Pathak, Pawan Goyal, and Plaban Bhowmick. 2016. A Two-Phase Approach Towards Identifying Argument Structure in Natural Language. In Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016), pages 11–19, Osaka, Japan. The COLING 2016 Organizing Committee.