Intent Role Oriented Query Parsing and Its Application in Subtopic Mining

Nowadays, web search engines are playing an increasingly dominant role in people’s daily information access on the web. But the majority of users are submitting short queries, many of which are ambiguous and/or underspecified. Thus, understanding the underlying intent or information need encoded within a query has become an essential step for effective information retrieval. Based on the statement that the composing terms play different roles in manifesting the information need or intent encoded in a query, this thesis proposes the strategy of intent role oriented query parsing. Two intent roles, kernel-object andmodifier, are defined, based on which queries are further classified as role-explicit queries and role-implicit queries. In the so-called intent role oriented query parsing, several fundamental issues are studied: (i) Role-explicit query extraction, which aims to extract a repository of role-explicit queries. In particular, this thesis separates all sessions in a query log into mul-sessions and sin-sessions. A number of features in the context of a session are used to quantify the weight of a term being the kernel-object. Furthermore, according to the session type, different approaches are proposed to extract role-explicit queries. (ii) Intent role annotation, which refers to identifying the composing kernel-object and modifier of a given role-explicit query. This thesis proposes the simplified n-gram role language model to perform intent role annotation, which achieves a satisfactory performance, more than 73% in terms of the term-level and query-level metrics. (iii) Role-explicit query identification, which refers to determining whether or not an arbitrary query is a role-explicit query. Using a set of predictive features, this thesis investigates a number of machine learning classifiers to perform role-explicit query identification. The experimental results show that the classifiers can achieve more than 90% precision in identifying role-explicit queries. Finally, this thesis investigates how intent role oriented query parsing can be adapted and integrated into the subtopic mining task. Intuitively, the concept of modifier graph is proposed. When the edges are weighted using the term-level knowledge in query log, the clustering of a modifier graph uncovers the underlying subtopics with respect to a particular topic. Then the problem of subtopic mining is reduced to the clustering of modifier graph. Based on this idea, the strategy of subtopic mining via modifier graph clustering is proposed. Leveraging on the standard test collection and data released by NTCIR-10, a series of experiments are conducted. The experimental results show that the proposed approaches based on modifier graph clustering are robust to the sparseness problem and outperforms the two baseline methods in terms of I-rec, D-nDCG and D#nDCG. The central contributions of this thesis are the introduction of intent role oriented query parsing and its application in subtopic mining. In deed, the intent role oriented query parsing is worthy to be further explored in many other applications, e.g., geographic intent analysis, document re-ranking in information retrieval.