A New Keyphrases Extraction Method Based on Suffix Tree Data Structure for Arabic Documents Clustering

Sahmoudi, Issam; Froud, Hanane; Lachkar, Abdelmonaime

doi:10.5121/ijdms.2013.5602

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…The top phrases are output as the descriptive topic of the document cluster. The KPMiner system proposed in [22]for Arabic language contains three logical steps: Candidate Keyphrases selection, Candidate Keyphrases weight calculation and finally Candidate Phrases List Refinement extracts Arabic Key Phrases [18]. Another work is Suffix Tree based Phrase extraction.…”

Section: Unsupervised Learningmentioning

confidence: 99%

Labeling Document Clusters with Thematic Phrases

Lalitha¹,

Raju²,

Rao³

2017

International Advanced Research Journal in Science, Engineering

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Document clustering is a powerful technique to detect topics and their relations for information browsing, analysis, and organization. However, clustered documents require post-assignment of descriptive titles to help users interpret the results. Existing techniques often assign labels to clusters based only on the terms that the clustered documents contain, which may not be sufficient for some applications more over term labeling will not give clear meaning of the clustered contents. To solve the problem, a phrase based cluster labeling is considered in this work. The work considers embedding external knowledge to terms using WordNet and provides an approach to derive a theme in the group of documents and label that group with the most appropriate Phrase. Number of experiments conducted on benchmark datasets and observed that results produced are very accurate to the clusters formed.

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Section: Unsupervised Learningmentioning

confidence: 99%

Labeling Document Clusters with Thematic Phrases

Lalitha¹,

Raju²,

Rao³

2017

International Advanced Research Journal in Science, Engineering

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“…Significant information in this paper is referring to the words and phrases, also called as keyphrase [15] which are important and meaningful information for a specific subject in the entire collections. A keyphrase is described as important and significant information as a greatly shortened summary for a document [4].…”

Section: Significant Information Extractionmentioning

confidence: 99%

“…Keyphrase extraction is given a more focus from the previous research for journal or conference articles summarizations. Also, the researchers mostly applied for English language dataset [15] in NLP study. This paper however uses Keyphrase Extraction in IR methods.…”

Section: Significant Information Extractionmentioning

confidence: 99%

A Survey: Framework of an Information Retrieval for Malay Translated Hadith Document

2017

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Abstract. This paper reviews and analyses the limitation of the existing method used in the IR process in retrieving Malay Translated Hadith documents related to the search request. Traditional Malay Translated Hadith retrieval system has not focused on semantic extraction from text. The bag-of-words representation ignores the conceptual similarity of information in the query text and documents, which produce unsatisfactory retrieval results. Therefore, a more efficient IR framework is needed. This paper claims that the significant information extraction and subject-related information are actually important because the clues from this information can be used to search and find the relevance document to a query. Also, unimportant information can be discarded to represent the document content. So, semantic understanding of query and document is necessary to improve the effectiveness and accuracy of retrieval results for this domain study. Therefore, advance research is needed and it will be experimented in the future work. It is hoped that it will help users to search and find information regarding to the Malay Translated Hadith document.

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