2026-09-19T02:40:15Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/50592 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Zhai, ChengXiang Zhai, ChengXiang Han, Jiawei Schatz, Bruce R. Roth, Dan Hsu, Bo-June Li, Yanen 2014-09-16T17:24:12Z 2014-09-16T17:24:12Z 2014-08 2014-09-16 2014-08 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. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2014-07-16T20:15:42Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Li_Yanen.pdf: 1793022 bytes, checksum: 696fe0bca7a69cf25c5d9ce8f3eef3cd (MD5) Made available in DSpace on 2014-09-16T17:24:12Z (GMT). No. of bitstreams: 2 Yanen_Li.pdf: 1792940 bytes, checksum: fabb9167a1717c6cb8cc1cc02ce0af7c (MD5) license.txt: 4058 bytes, checksum: e5bc61ce4ed1d3cf3c2650f914a1a124 (MD5) http://hdl.handle.net/2142/50592 en Copyright 2014 Yanen Li Web Search Query Understanding Multi-Level Query Understanding Query Spelling Correction Query Segmentation Query Semantics Query Auto-Completion A systematic study of multi-level query understanding text Computer Science 1434 Computer Science 0112 University of Illinois at Urbana-Champaign Dissertation Ph.D. PHD:Computer Science -UIUC 10KS0112PHD