Web text mining method based on subtopic selection and three-level stratified structure

Yuzhen SHI; Donghong SHAN

doi:10.11959/j.issn.1000-0801.2016142

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Web text mining method based on subtopic selection and three-level stratified structure

research and development | 更新时间：2024-06-05

- Web text mining method based on subtopic selection and three-level stratified structure
- Observation and communication Vol. 32, Issue 5, Pages: 96-104(2016)
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- DOI：10.11959/j.issn.1000-0801.2016142
  CLC： TP391
- Published Online：2016-05，
  
  Published：20 May 2016
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Yuzhen SHI, Donghong SHAN. Web text mining method based on subtopic selection and three-level stratified structure[J]. Observation and communication, 2016, 32(5): 96-104.
DOI：

Yuzhen SHI, Donghong SHAN. Web text mining method based on subtopic selection and three-level stratified structure[J]. Observation and communication, 2016, 32(5): 96-104. DOI： 10.11959/j.issn.1000-0801.2016142.

摘要

针对用户和查询之间的意图差距导致的查询模糊宽泛和数据稀疏问题，根据流行性和多样性返回可能子主题的排名列表，利用子主题选择与排序的分层结构进行Web 文本挖掘。首先，在名词性短语和可替代部分查询的基础上，使用简单模式提取各种相关的短语作为候选子主题；然后，使用网页文档集合中的相关文档构建候选子主题的三级层次结构；最后，综合考虑流行性和多样性，利用该结构和估计的流行度进行排序。实验使用了NTCIR-9库的100个日文查询和来自TREC 2009库的100个英文查询以及网络跟踪多样性任务，实验结果验证了本文方法可有效应用于各种搜索，对于高排名的子主题挖掘优于外部资源。

Abstract

As the problem of fuzzy inquiry and data sparseness cased by intention gap between users and queries

according to the ranking list of possible subtopic from popularity and diversity

subtopic selection and sorting of stratified structure were used for web text mining.Firstly

on the basic of noun phrase and substitute of part query

a simple model was used to extract a variety of related phrases as candidate subtopic.Then

related documents of a web document collection were used to build three-level stratified structure of candidate subtopic.Finally

considering popularity and diversity

the stratified structure and estimated popularity were applied for sorting.Based on 100 Japanese queries from NTCIR-9 library

100 English queries from TREC 2009 library and network tracking diversity task

experiments verify that the proposed method can be effectively applied to a variety of search

and the proposed mining is better than external resources for high ranking subtopics.

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references

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