MODEL GENERATION DEVICE, CLICK-LOG CORRECT-ANSWER LIKELIHOOD CALCULATION DEVICE, DOCUMENT RETRIEVAL DEVICE, METHOD, AND PROGRAM

摘要

PROBLEM TO BE SOLVED: To enable the improvement in retrieval accuracy.;SOLUTION: Concept vector generation means 54 synthesizes, for a text of each query and each document in a click log, concept vectors of words in the text in a word concept base 52, to thereby create a concept vector for the text. Then, feature vector generation means 56 extracts, for an arbitrary pair in the click log, a feature including the number of in-neighborhood pairs or the number of difference users tied up to in-neighborhood pair provided that such a pair in the click log that a query concept vector exists in a neighborhood of a concept vector of the pair of queries and a document concept vector exists in a neighborhood of a concept vector of the pair of documents is taken as an in-neighborhood pair, to thereby create a feature vector for the pair. Then, correct-answer likelihood estimation means 62 estimates, for an arbitrary pair in the click log, a correct-answer likelihood of the pair by means of the pair of feature vectors and the classification model.;SELECTED DRAWING: Figure 13;COPYRIGHT: (C)2018,JPO&INPIT