[1]蔡桂秀,王明文,揭安全,等.基于Markov网络团的查询意图识别[J].江西师范大学学报(自然科学版),2012,(04):383-387.
　CAI Gui-xiu,WANG Ming-wen,JIE An-quan,et al.A Method for Query Intent Identification Based on Markov Network Clique[J].Journal of Jiangxi Normal University:Natural Science Edition,2012,(04):383-387.
点击复制

基于Markov网络团的查询意图识别()
分享到：

《江西师范大学学报》（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2012年04期
页码:
383-387
栏目:
出版日期:
2012-08-01

文章信息/Info

Title:
A Method for Query Intent Identification Based on Markov Network Clique
作者:
蔡桂秀;王明文;揭安全;王晓庆
江西师范大学计算机信息工程学院,江西 南昌 330022
Author(s):
CAI Gui-xiu;  WANG Ming-wen;  JIE An-quan;  WANG Xiao-qing
关键词:
查询分类; Markov网络; 文本分类; 11_avg; 3_avg
Keywords:
query classification;  Markov network;  text classification;  11_avg;  3_avg
分类号:
TP391.1
文献标志码:
A
摘要:
通过利用 Markov网络团的方法来对查询意图识别.首先从人工标注搜狗查询日志中约2250个查询作为测试数据,采用搜狗提供的分类语料(共10类)来建立Markov网络,用建立的Markov网络来对查询进行扩展,得到相关的返回结果列表,运用在分类语料训练好的分类器来对返回结果进行分类,从而完成对查询意图识别的过程.实验中采用的评价指标是11_avg 和3_avg,实验结果表明该方法能够有效地提高检索效率.
Abstract:
A new method about query intent classification is proposed. Making use of manually labeled queries form Sogou’s query log (about 2 250) as training data, and use the ten classes of data to construct the Markov network. So we can effectively get information of the queries. After this process research the queries in this data. Returning the relevance results of the queries, and classifying these results according to the classifier trained by the ten classes data. At last, the queries intent is predicted. In the experiments use the 11_avg and 3_avg as assessment process. Experiment results demonstrate that the algorithm presents some advantages compared with other methods.

参考文献/References:

[1] 张森, 王斌, 张磊. Web检索查询意图分类技术综述 [J]. 中文信息学报, 2008, 22(4): 75-82.
[2] Ashkan A, Clarke C, Agichtein E, et al. Characterizing query intent from sponsored search clickthrough data [EB/OL].
[2012-02-16]. http://www.mathcs.emory.edu/~qguo3/ira2008-azin. pdf.
[3] Hu Jian, Wang Gang, Lochovsky F, et al. Understanding user's query intent with Wikipedia [C]. New York: ACM, 2009: 471-480.
[4] Beitzel S, Jensen E, Frieder O, et al. Automatic web query classification using labeled and unlabeled training data [C]. New Youk: ACM, 2005: 581-582.
[5] Liu Yiqun, Zhang Min, Ru Liyun, et al. Automatic query type identification based on click through information [J]. Lecture Notes in Computer Science, 2006, 4182: 593-600.
[6] Guo Qi, Agichtein E. Exploring mouse movements for inferring query intent [EB/OL]. http://www.mathcs.emory.deu/~eugene papers/sigir2008p-mouse-moves.pdf.
[7] Xiao Li, Wang Yeyi, Alex Acero. Learning query intent from regularized click graphs. [EB/OL].
[2012-02-26]. http://research. Microsoft.com/pubs/75219/sigir2008.pdf.
[8] 甘丽新, 王明文, 张华伟. 基于团的Markov网络信息检索模型[EB/OL].
[2012-03-19]. http://www.docin.com/p-193591277. tml..
[9] 左佳莉, 王明文, 王希. 基于Markov 网络的信息检索扩展模型 [J]. 清华大学学报: 自然科学版, 2005, 45: 1847-1852. Makoto Iwayama, Astushi Fujii, Noriko Kando, et al. An empirical study on retrieval models for different document genres: patents and newspaper articles[C]. New York: ACM, 2003: 251-258.

更新日期/Last Update: 1900-01-01