A systematic study of multi-level query understanding

Search and information retrieval technologies have significantly transformed the way people seek information and acquire knowledge from the internet. To further improve the search accuracy and usability of the current-generation search engines, one of the most important research challenges is for a search engine to accurately understand a user’s intent or information need underlying the query. This thesis presents a systematic study of query understanding. In this thesis I have proposed a conceptual framework where there are different levels of query understanding. And these levels of query understanding have natural logical dependency. After that, I will present my studies on addressing important research questions in this framework. First, as a major type of query alteration, I addressed the query spelling correction problem by modeling all major types of spelling errors with a generalized Hidden Markov Model. Second, query segmentation is the most important type of query linguistic signals. I proposed a probabilistic model to identify the query segmentations using clickthrough data. Third, synonym finding is an important challenge for semantic annotation of queries. I proposed a compact clustering framework to mine entity attribute synonyms for a set of inputs jointly with multiple information sources. And finally, in the dynamic query understanding, I introduced the horizontal skipping bias which is unique to the query auto- completion process (QAC). I then proposed a novel two-dimensional click model for modeling the QAC process with emphasis on such behavior