TY - JOUR
T1 - Method for selecting appropriate sentence from documents on the WWW for the open-ended conversation dialog system
AU - Shibata, Masahiro
AU - Tomiura, Yoichi
AU - Nishiguchi, Tomomi
PY - 2009
Y1 - 2009
N2 - We propose an open-ended dialog system that generates a proper sentence to a user's utterance using abundant documents on the World Wide Web as sources. Existing knowledge-based dialog systems give meaningful information to a user, but they are unsuitable for open-ended input. The system Eliza can handle open-ended input, but it gives no meaningful information. Our system lies between the above two dialog systems; it converses on various topics and gives meaningful information related to the user's utterances. The system selects an appropriate sentence as a response from documents gathered through the Web, on the basis of surface cohesion and shallow semantic coherence. The surface cohesion follows centering theory and the semantic coherence is calculated on the basis of the conditional distribution and inverse document frequency of content words (nouns, verbs, and adjectives.) We developed a trial system to converse about movies and experimentally found that the proposed method generated 66 % appropriate responses.
AB - We propose an open-ended dialog system that generates a proper sentence to a user's utterance using abundant documents on the World Wide Web as sources. Existing knowledge-based dialog systems give meaningful information to a user, but they are unsuitable for open-ended input. The system Eliza can handle open-ended input, but it gives no meaningful information. Our system lies between the above two dialog systems; it converses on various topics and gives meaningful information related to the user's utterances. The system selects an appropriate sentence as a response from documents gathered through the Web, on the basis of surface cohesion and shallow semantic coherence. The surface cohesion follows centering theory and the semantic coherence is calculated on the basis of the conditional distribution and inverse document frequency of content words (nouns, verbs, and adjectives.) We developed a trial system to converse about movies and experimentally found that the proposed method generated 66 % appropriate responses.
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U2 - 10.1527/tjsai.24.507
DO - 10.1527/tjsai.24.507
M3 - Article
AN - SCOPUS:70350128796
SN - 1346-0714
VL - 24
SP - 507
EP - 519
JO - Transactions of the Japanese Society for Artificial Intelligence
JF - Transactions of the Japanese Society for Artificial Intelligence
IS - 6
ER -