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Erschienen in:
Combining Node Identifier Features and Community Priors for Within-Network Classification Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

An Active Learning Approach to Recognizing Domain-Specific Queries From Query Log

verfasst von : Weijian Ni, Tong Liu, Haohao Sun, Zhensheng Wei

Erschienen in: Web and Big Data

Verlag: Springer International Publishing

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Abstract

In this paper, we address the problem of recognizing domain-specific queries from general search engine’s query log. Unlike most previous work in query classification relying on external resources or annotated training queries, we take query log as the only resource for recognizing domain-specific queries. In the proposed approach, we represent query log as a heterogeneous graph and then formulate the task of domain-specific query recognition as graph-based transductive learning. In order to reduce the impact of noisy and insufficient of initial annotated queries, we further introduce an active learning strategy into the learning process such that the manual annotations needed are reduced and the recognition results can be continuously refined through interactive human supervision. Experimental results demonstrate that the proposed approach is capable of recognizing a certain amount of high-quality domain-specific queries with only a small number of manually annotated queries.

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Vorheriges Kapitel Combining Node Identifier Features and Community Priors for Within-Network Classification
Nächstes Kapitel Event2vec: Learning Representations of Events on Temporal Sequences
Fußnoten
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Metadaten
Titel
An Active Learning Approach to Recognizing Domain-Specific Queries From Query Log
verfasst von
Weijian Ni
Tong Liu
Haohao Sun
Zhensheng Wei
Copyright-Jahr
2017
Buch
Web and Big Data

Print ISBN: 978-3-319-63563-7

Electronic ISBN: 978-3-319-63564-4

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-63564-4_2