NDLI: A new approach to query segmentation for relevance ranking in web search

Content Provider	Springer Nature Link
Author	Wu, Haocheng Hu, Yunhua Li, Hang Chen, Enhong
Copyright Year	2014
Abstract	In this paper, we try to determine how best to improve state-of-the-art methods for relevance ranking in web searching by query segmentation. Query segmentation is meant to separate the input query into segments, typically natural language phrases. We propose employing the re-ranking approach in query segmentation, which first employs a generative model to create the top k candidates and then employs a discriminative model to re-rank the candidates to obtain the final segmentation result. The method has been widely utilized for structure prediction in natural language processing, but has not been applied to query segmentation, as far as we know. Furthermore, we propose a new method for using the results of query segmentation in relevance ranking, which takes both the original query words and the segmented query phrases as units of query representation. We investigate whether our method can improve three relevance models, namely n-gram BM25, key n-gram model and term dependency model, within the framework of learning to rank. Our experimental results on large scale web search datasets show that our method can indeed significantly improve relevance ranking in all three cases.
Starting Page	26
Ending Page	50
Page Count	25
File Format	PDF
ISSN	13864564
Journal	Information Retrieval
Volume Number	18
Issue Number	1
e-ISSN	15737659
Language	English
Publisher	Springer Netherlands
Publisher Date	2014-09-28
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Web search Query segmentation Relevance ranking Query processing Re-ranking BM25 Term dependency model Key n-gram extraction Information Storage and Retrieval Document Preparation and Text Processing Data Mining and Knowledge Discovery Data Structures, Cryptology and Information Theory Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Library and Information Sciences Information Systems

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