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  1. Proceedings of the First International Workshop on Keyword Search on Structured Data (KEYS '09)
  2. Query segmentation using conditional random fields
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Structured data and web documents: better together?
Querying text databases and the web: beyond traditional keyword search
Hierarchical result views for keyword queries over relational databases
Query segmentation using conditional random fields
Building search applications with Marklogic Server
Efficient top-k algorithms for fuzzy search in string collections
Keyword query cleaning using hidden Markov models
XML keyword query refinement
A first study on strategies for generating workflow snippets
Do we mean the same?: disambiguation of extracted keyword queries for database search
Language-model-based ranking in entity-relation graphs
Generic and effective semi-structured keyword search
DBDOC: querying and browsing databases and interrelated documents

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Query segmentation using conditional random fields

Content Provider ACM Digital Library
Author Yu, Xiaohui Shi, Huxia
Abstract A growing mount of available text data are being stored in relational databases, giving rise to an increasing need for the RDBMSs to support effective text retrieval. In this paper, we address the problem of keyword query segmentation, i.e., how to group nearby keywords in a query into segments. This operation can greatly benefit both the quality and the efficiency of the subsequent search operations. Compared with previous work, the proposed approach is based on Conditional Random Fields (CRF), and provides a principled statistical model that can be learned from query logs and easily adapt to user preferences. Extensive experiments on two real datasets confirm the effectiveness the efficiency of the proposed approach.
Starting Page 21
Ending Page 26
Page Count 6
File Format PDF
ISBN 9781605585703
DOI 10.1145/1557670.1557680
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2009-06-28
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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