A Latent Semantic Indexing-based approach to multilingual document clustering

Wei, Chih-Ping; Yang, Christopher C.; Lin, Chia-Min

doi:10.1016/j.dss.2007.07.008

Cited by 74 publications

(19 citation statements)

References 34 publications

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“…In parallel corpus the source language query retrieves source language documents. The corresponding target-language documents are then used to extract a set of terms in the target language.. Wei et.al [17] proposed that the necessity of Multilingual document clustering (MLDC) is to maintain documents belonging to different languages in knowledge repositories to build an organizational knowledge maps.…”

Section: Corpus Based Translationmentioning

confidence: 99%

Translation approaches in Cross Language Information Retrieval

Raju¹,

Raju²,

Satyanarayana

2014

International Conference on Computing and Communication Technologies

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Cross-Language Information Retrieval (CLIR) is a sub field of Information Retrieval (IR). Like IR, in CLIR for a particular information need, we have to find relevant information or documents containing such information. In CLIR multiple tools must be developed to match terms containing same meaning in different languages. The usual solution is to translate the query and/or the documents before performing the search. So translation is pivotal activity in CLIR. In CLIR a wide range of techniques were proposed in the literature for translation. This paper presents a detailed insight of these techniques, with a special emphasis on recent developments.

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Section: Corpus Based Translationmentioning

confidence: 99%

Translation approaches in Cross Language Information Retrieval

Raju¹,

Raju²,

Satyanarayana

2014

International Conference on Computing and Communication Technologies

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“…But this method requires a sophisticated automatic term extraction algorithms to extract the terms automatically from a document. Wei et al, (2008) proposed an approach called Latent Semantic Indexing (LSI) [13] 45 which preserves the representative features for a document. The LSI preserves the most representative features rather than discriminating features.…”

Section: Related Workmentioning

confidence: 99%

Text Document Classification: An Approach Based on Indexing

Harish¹

2012

IJDKP

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“…But this method requires a sophisticated automatic term extraction algorithms to extract the terms automatically from a document. Wei et al, (2008) used an approach called Latent Semantic Indexing (LSI) [11] which preserves the representative features for a document. The LSI preserves the most representative features rather than discriminating features.…”

Section: Related Workmentioning

confidence: 99%

Symbolic representation of text documents

Guru

Harish

Manjunath

2010

Proceedings of the Third Annual ACM Bangalore Conference

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This paper presents a novel method of representing a text document by the use of interval valued symbolic features. A method of classification of text documents based on the proposed representation is also presented. The newly proposed model significantly reduces the dimension of feature vectors and also the time taken to classify a given document. Further, extensive experimentations are conducted on vehicles-wikipedia datasets to evaluate the performance of the proposed model. The experimental results reveal that the obtained results are on par with the existing results for vehicles-wikipedia dataset. However, the advantage of the proposed model is that it takes relatively a less time for classification as it is based on a simple matching strategy.

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A Latent Semantic Indexing-based approach to multilingual document clustering

Cited by 74 publications

References 34 publications

Translation approaches in Cross Language Information Retrieval

Translation approaches in Cross Language Information Retrieval

Text Document Classification: An Approach Based on Indexing

Symbolic representation of text documents

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