Textual Supportiveness Recognition Based on Combinations of Syntax Features for Automated Argument Generation

• Published in: Transactions of the Japanese Society for Artificial Intelligence Vol. 31; no. 6; pp. AI30-L_1 - 12
• Main Authors: Sato, Misa, Yanai, Kohsuke, Yanase, Toshihiko, Miyoshi, Toshinori, Koreeda, Yuta, Niwa, Yoshiki
• Format: Journal Article
• Language: Japanese; English
• Published: Tokyo The Japanese Society for Artificial Intelligence 01.11.2016; Japan Science and Technology Agency
• Subjects: argumentation; Data mining; debating artificial intelligence; Employment; Feature recognition; Hypotheses; natural language processing; Object recognition; Sentiment analysis; Syntax; text generation
• ISSN: 1346-0714; 1346-8030
• DOI: 10.1527/tjsai.AI30-L

This paper describes a technique to recognize “supportiveness” of a given text for an argument topic object and a value. Given an argument topic object (o), a value (v), and a text fragment (t), supportiveness refers to whether t supports a hypothesis “o promotes/suppresses v” or not. For example, with “o: casino” and “v: employment”, then a text “The casinos in Mississippi have created 35,000 jobs.” should support a hypothesis “o promotes v”. This technique enables to automatically collect texts representing reasons and counterexamples for some hypothesis that humans build up (e.g. “casino promotes employment”), combined with text search. Because the difference from relation extraction is polarity of relations, proposed method utilizes multiplifications based on local syntax structures, extending reversing hypothesis in sentiment analysis. We propose feature combinations consisting of “primary features” and “secondary features” for supportiveness recognition. “Primary features” represent local syntax structures around a given target or a given value. “Secondary features” represent global syntax structures generated by combining the primary features. The proposed method calculates weighted sum of secondary features to recognize promoting/suppressing supportiveness. The experiments showed that our method outperforms a Bag-of-Words baseline and a conventional relation extraction method.