Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
11-2007
Abstract
Searching answers to complex questions is a challenging IR task. In this paper, we examine the use of query templates with semantic slots to formulate slot-based queries. These queries have query terms assigned to entity and relationship slots. We develop several query expansion methods for slot-based queries so as to improve their retrieval effectiveness on a document collection. Each method consists of a combination of term scoring scheme, term scoring formula, and term assignment scheme. Our preliminary experiments evaluate these different slot-based query expansion methods on a collection of news documents,and conclude that:(1) slot-based queries yield better retrieval accuracy compared to keyword-based queries in the complex question problems; and (2)directly applying traditional query expansion on the query terms of each slot does not always work well.
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Publication
Workshop on CyberInfrastructure: Information Management in eScience (CIMS2007), In conjunction with ACM 9th International Workshop on Web Information and Data Management (WIDM 2007)
Identifier
10.1145/1317353.1317364
Publisher
ACM
City or Country
Lisboa, Portugal
Citation
SURYANTO, Maggy Anastasia; LIM, Ee Peng; SUN, Aixin; and CHIANG, Roger Hsiang-Li.
SLOQUE: Slot-based Query Expansion for complex questions. (2007). Workshop on CyberInfrastructure: Information Management in eScience (CIMS2007), In conjunction with ACM 9th International Workshop on Web Information and Data Management (WIDM 2007).
Available at: https://ink.library.smu.edu.sg/sis_research/1263
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1145/1317353.1317364
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons