Exploiting noun phrases and semantic relationships for text document clustering

Zheng, Haitao; Kang, Bo‐Yeong; Kim, Hong-Gee

doi:10.1016/j.ins.2009.02.019

Cited by 77 publications

(44 citation statements)

References 47 publications

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“…they don't consider other semantic relationships. To address these issues, in [12] authors have combined detection of noun phrases and WordNet. This integration helps in exploring documents more semantically for clustering purpose.…”

Section: Review Of Semantic Driven Document Clustering Methodsmentioning

confidence: 99%

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Semantic based Document Clustering: A Detailed Review

Shah¹,

Mahajan²

2012

IJCA

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Section: Review Of Semantic Driven Document Clustering Methodsmentioning

confidence: 99%

“…In [6] Clustering based on Frequent Word Sequences (CFWS) has been proposed. In [12] the authors have proposed various document representation methods to exploit noun phrases and semantic relationships for clustering. Using WordNet, hypernymy, hyponymy, holonymy, and meronymy have been utilized for clustering.…”

Section: Overview Of Clustering Algorithmsmentioning

confidence: 99%

Semantic based Document Clustering: A Detailed Review

Shah¹,

Mahajan²

2012

IJCA

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“…In [14] the authors have proposed various document representation methods to exploit noun phrases and semantic relationships for clustering. Using WordNet, hypernymy, hyponymy, holonymy, and meronymy have been utilized for clustering.…”

Section: Related Workmentioning

confidence: 99%

“…The ordering is in a way that more commonly used terms are listed before less commonly used terms. It has been showed that using the first synset as identified concept for a term can improve the clustering performance more than that of using all the synsets to calculate concept frequencies [14]. In this paper, we also use only the first synset as the concept for a term for computing concept frequencies.…”

Section: Parts Of Speech Taggingmentioning

confidence: 99%

A Frequent Concepts Based Document Clustering Algorithm

Baghel¹,

Dhir²

2010

IJCA

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This paper presents a novel technique of document clustering based on frequent concepts. The proposed technique, FCDC (Frequent Concepts based document clustering), a clustering algorithm works with frequent concepts rather than frequent items used in traditional text mining techniques. Many well known clustering algorithms deal with documents as bag of words and ignore the important relationships between words like synonyms. the proposed FCDC algorithm utilizes the semantic relationship between words to create concepts. It exploits the WordNet ontology in turn to create low dimensional feature vector which allows us to develop a efficient clustering algorithm. It uses a hierarchical approach to cluster text documents having common concepts. FCDC found more accurate, scalable and effective when compared with existing clustering algorithms like Bisecting K-means , UPGMA and FIHC.

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“…Traditional clustering techniques reply on three concepts: representation model [2]- [4], similarity measure [5] and clustering model [6]. However, these traditional models have a number of limitations.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Digital Book Clustering by LDAC Model

Wang

Yuan

2012

IEICE Trans. Inf. & Syst.

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SUMMARYIn Digital Library (DL) applications, digital book clustering is an important and urgent research task. However, it is difficult to conduct effectively because of the great length of digital books. To do the correct clustering for digital books, a novel method based on probabilistic topic model is proposed. Firstly, we build a topic model named LDAC. The main goal of LDAC topic modeling is to effectively extract topics from digital books. Subsequently, Gibbs sampling is applied for parameter inference. Once the model parameters are learned, each book is assigned to the cluster which maximizes the posterior probability. Experimental results demonstrate that our approach based on LDAC is able to achieve significant improvement as compared to the related methods.

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Exploiting noun phrases and semantic relationships for text document clustering

Cited by 77 publications

References 47 publications

Semantic based Document Clustering: A Detailed Review

Semantic based Document Clustering: A Detailed Review

A Frequent Concepts Based Document Clustering Algorithm

Enhancing Digital Book Clustering by LDAC Model

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