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Incorporating semantic and syntactic...

Wang, Yong.

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Incorporating semantic and syntactic information into document representation for document clustering.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Incorporating semantic and syntactic information into document representation for document clustering./
作者:	Wang, Yong.
面頁冊數:	134 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-10, Section: B, page: 5514.
Contained By:	Dissertation Abstracts International66-10B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3193781
ISBN:	9780542370540

Incorporating semantic and syntactic information into document representation for document clustering.
Wang, Yong.

Incorporating semantic and syntactic information into document representation for document clustering. - 134 p.

Source: Dissertation Abstracts International, Volume: 66-10, Section: B, page: 5514.

Thesis (Ph.D.)--Mississippi State University, 2005.

Document clustering is a widely used strategy for information retrieval and text data mining. In traditional document clustering systems, documents are represented as a bag of independent words. In this project, we propose to enrich the representation of a document by incorporating semantic information and syntactic information. Semantic analysis and syntactic analysis are performed on the raw text to identify this information. A detailed survey of current research in natural language processing, syntactic analysis, and semantic analysis is provided. Our experimental results demonstrate that incorporating semantic information and syntactic information can improve the performance of our document clustering system for most of our data sets. A statistically significant improvement can be achieved when we combine both syntactic and semantic information. Our experimental results using compound words show that using only compound words does not improve the clustering performance for our data sets. When the compound words are combined with original single words, the combined feature set gets slightly better performance for most data sets. But this improvement is not statistically significant. In order to select the best clustering algorithm for our document clustering system, a comparison of several widely used clustering algorithms is performed. Although the bisecting K-means method has advantages when working with large datasets, a traditional hierarchical clustering algorithm still achieves the best performance for our small datasets.

ISBN: 9780542370540Subjects--Topical Terms:

626642
Computer Science.

Incorporating semantic and syntactic information into document representation for document clustering.
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502 $a Thesis (Ph.D.)--Mississippi State University, 2005.
520 $a Document clustering is a widely used strategy for information retrieval and text data mining. In traditional document clustering systems, documents are represented as a bag of independent words. In this project, we propose to enrich the representation of a document by incorporating semantic information and syntactic information. Semantic analysis and syntactic analysis are performed on the raw text to identify this information. A detailed survey of current research in natural language processing, syntactic analysis, and semantic analysis is provided. Our experimental results demonstrate that incorporating semantic information and syntactic information can improve the performance of our document clustering system for most of our data sets. A statistically significant improvement can be achieved when we combine both syntactic and semantic information. Our experimental results using compound words show that using only compound words does not improve the clustering performance for our data sets. When the compound words are combined with original single words, the combined feature set gets slightly better performance for most data sets. But this improvement is not statistically significant. In order to select the best clustering algorithm for our document clustering system, a comparison of several widely used clustering algorithms is performed. Although the bisecting K-means method has advantages when working with large datasets, a traditional hierarchical clustering algorithm still achieves the best performance for our small datasets.
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