User Information Extraction for Personalized Dialogue Systems
Tôru Hirano, Nozomi Kobayashi, Ryuichiro Higashinaka, Toshiro Makino, Yoshihiro Matsuo · Transactions of the Japanese Society for Artificial Intelligence · 2015
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 , 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.