Query mining based on term relationship estimation in verbal queries

Abstract

In this paper, we propose a method to find query suggestions of a verbal query, which contains a verb in the query, from the Web. People sometimes cannot obtain appropriate search results even if they consider they have formulated a query that clearly describes their search intents. The idea of the proposed method is to find the relationship between verb and noun in the query, and mine the appropriate representation of the verb based on the relationship. The proposed method estimates the relationship between verb and noun based on particles between them. Based on the estimated relationship, we then obtain candidates of the verb in the query by using either the Web search results or the case frame. Next, we compute the effectiveness of the candidates by considering the similarity between a candidate and the verb and the co-occurrence between the candidates and the noun, and finally rank the candidates to generate queries. To investigate the effectiveness of our proposed method, we conducted the experiment by comparing with the query suggestions of a commercial search engine as our baseline. The experimental result of 20 queries showed that our proposed method, which finds candidates from the Web search results, outperformed the baseline method in terms of AvgRelNum, which measures the the number of relevant pages obtained by the generated query that can retrieve a relevant page, and achieved the similar performance in terms of Contain@10 and MRR@10.