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Reseach Article

A Query Classification System based on Snippet Similarity for a One-Click Search

by Tatsuya Tojima, Takashi Yukawa
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 82 - Number 1
Year of Publication: 2013
Authors: Tatsuya Tojima, Takashi Yukawa
10.5120/14077-2146

Tatsuya Tojima, Takashi Yukawa . A Query Classification System based on Snippet Similarity for a One-Click Search. International Journal of Computer Applications. 82, 1 ( November 2013), 1-8. DOI=10.5120/14077-2146

@article{ 10.5120/14077-2146,
author = { Tatsuya Tojima, Takashi Yukawa },
title = { A Query Classification System based on Snippet Similarity for a One-Click Search },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 82 },
number = { 1 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume82/number1/14077-2146/ },
doi = { 10.5120/14077-2146 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:56:37.506187+05:30
%A Tatsuya Tojima
%A Takashi Yukawa
%T A Query Classification System based on Snippet Similarity for a One-Click Search
%J International Journal of Computer Applications
%@ 0975-8887
%V 82
%N 1
%P 1-8
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes a query classification system for a one-click search system that uses feature vectors based on snippet similarity. The proposed system targets the NTCIR-10 1CLICK-2 query classification subtask and classifies queries in Japanese and English into eight predefined classes by using support vector machines (SVMs). In the NTCIR-9 and NTCIR-10 tasks, most participants used complex features or rules that depend strongly on language characteristics. The authors propose a new method that uses feature vectors created by using snippet similarities instead of the above mentioned features. In the proposed method, feature vectors have fewer dimensions, provide better generalization, lower language dependency, and reduced computer resources. This method achieved accuracies of 0. 93 for a Japanese task and 0. 91 for an English task.

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Index Terms

Computer Science
Information Sciences

Keywords

Query Classification Dimension Reduction Intent Mobile