User Information Extraction for Personalized Dialogue Systems
We propose a method to extract user information in a structured form for personalized dialogue systems. Assuming that user information can be represented as a quadruple <predicate-argument structure, entity, attribute category, topic>, we focus on solving problems in extracting predicate argum...
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| Published in | Transactions of the Japanese Society for Artificial Intelligence Vol. 31; no. 1; pp. DSF-B_1 - 10 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | Japanese |
| Published |
The Japanese Society for Artificial Intelligence
08.01.2016
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| Subjects | |
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| Abstract | We propose a method to extract user information in a structured form for personalized dialogue systems. Assuming that user information can be represented as a quadruple <predicate-argument structure, entity, attribute category, topic>, we focus on solving problems in extracting predicate argument structures from question-answer pairs in which arguments and predicates are frequently omitted, and in estimating attribute categories related to user behavior which a method using only content words cannot distinguish. Experimental results show that the proposed method significantly outperformed baseline methods and was able to extract user information with 81.2% precision and 58.1% recall. |
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| AbstractList | We propose a method to extract user information in a structured form for personalized dialogue systems. Assuming that user information can be represented as a quadruple <predicate-argument structure, entity, attribute category, topic>, we focus on solving problems in extracting predicate argument structures from question-answer pairs in which arguments and predicates are frequently omitted, and in estimating attribute categories related to user behavior which a method using only content words cannot distinguish. Experimental results show that the proposed method significantly outperformed baseline methods and was able to extract user information with 81.2% precision and 58.1% recall. |
| Author | Hirano, Toru Kobayashi, Nozomi Matsuo, Yoshihiro Higashinaka, Ryuichiro Makino, Toshiro |
| Author_xml | – sequence: 1 fullname: Hirano, Toru organization: NTT Media Intelligence Laboratories, Nippon Telegraph and Telephone Corporation – sequence: 2 fullname: Kobayashi, Nozomi organization: NTT Media Intelligence Laboratories, Nippon Telegraph and Telephone Corporation – sequence: 3 fullname: Higashinaka, Ryuichiro organization: NTT Media Intelligence Laboratories, Nippon Telegraph and Telephone Corporation – sequence: 4 fullname: Makino, Toshiro organization: NTT Media Intelligence Laboratories, Nippon Telegraph and Telephone Corporation – sequence: 5 fullname: Matsuo, Yoshihiro organization: NTT Media Intelligence Laboratories, Nippon Telegraph and Telephone Corporation |
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| Copyright | The Japanese Society for Artificial Intelligence 2016 |
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| DOI | 10.1527/tjsai.DSF-512 |
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| Title | User Information Extraction for Personalized Dialogue Systems |
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