Multilingual Single Document Keyword Extraction for Information Retrieval

Bracewell, David B.; Ren, Fuji; Kuriowa, S.

doi:10.1109/nlpke.2005.1598792

Cited by 58 publications

(33 citation statements)

References 7 publications

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“…However, the focus of this paper lies on the topic discovery algorithm. Bracewell's algorithm [5] implemented the Bracewell's multilingual single document keyword extraction method [17] for the keywords extraction part which requires taggers and parsers to adapt with the different letters Japanese language use. Since Indonesian language uses the same alphabet letters with English, those components are no longer required.…”

Section: Proposed Methodsmentioning

confidence: 99%

Keywords Similarity Based Topic Identification for Indonesian News Documents

Fuddoly

Jaafar

Zamin

2013

2013 European Modelling Symposium

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Topic identification (TID) is a technique associated with labelling a set of textual documents with a meaningful label representing its content.TID for online news presents different problems from TID for other corpora, such as the large data volume and the frequently updated topic. Moreover, the number of developing methods for Indonesian corpus is rather small. Bracewell's algorithm has been proven effective in identifying topics in English and Japanese corpora with high accuracy. This paper implements a method for TID based on Bracewell's keywords similarity algorithm and the top-n keywords selection for Indonesian news documents. The top-n method is utilized to improve Bracewell's performance within Indonesian corpus, and to reduce the dimension of dataset during training. The combination is aimed to reduce the heavy computation problem and to explore the possibility of a new emerging topic which possibly has to be created. The method consists of two stages: training and classification. It studies the keywords of the training dataset then calculates the similarity between testing and training articles' keywords. The algorithm produced accuracy as high as 95.22% on onlineand95.26% on offline environment, 84% against human evaluation, and an average of 2.96 seconds computational time.

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Section: Proposed Methodsmentioning

confidence: 99%

Keywords Similarity Based Topic Identification for Indonesian News Documents

Fuddoly

Jaafar

Zamin

2013

2013 European Modelling Symposium

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“…, and then cluster the terms which have the same noun term [27]. The clusters are then ranked depending on term and noun phrase frequencies.…”

Section: Bracewell Et Al (2005) Extract Noun Phrases From a Documentmentioning

confidence: 99%

Keyword and Keyphrase Extraction Techniques: A Literature Review

Siddiqi¹,

Sharan²

2015

IJCA

180

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In this paper we present a survey of various techniques available in text mining for keyword and keyphrase extraction. Keywords and keyphrases are very useful in analyzing large amount of textual material quickly and efficiently search over the internet besides being useful for many other purposes. Keywords and keyphrases are set of representative words of a document that give high-level specification of the content for interested readers. They are used highly in the field of Computer Science especially in Information Retrieval and Natural Language Processing and can be used for index generation, query refinement, text summarization, author assistance, etc. We have also discussed some important feature selection metrics generally employed by researchers to rank candidate keywords and keyphrases according to their importance.

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“…As described in Section 2.1 in the classification process, we often make use of statistical methods and the threshold for selecting features and the words conceptual similarities and semantic relation does not have a role in the selection of features (Hotho et al, 2003) while background knowledge and ontologies can be used for semantic similarity of words (Bracewell et al, 2005;Fellbaum, 1998). Using ontology in addition to solving the problem of synonymous words provides taking the advantage of the general concepts and higher level concepts in the hierarchy of concepts.…”

Section: Features Selectionmentioning

confidence: 99%

Document features selection using background knowledge and word clustering technique

Farahmand¹,

Harounabadi²,

Mirabedini³

2014

10.5267/j.msl

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By everyday development of storage and communicational and electronic media, there are significant amount of information being collected and stored in different forms such as electronic documents and document databases makes it difficult to process them, properly. To extract knowledge from this large volume of documental data, we require the use of documents organizing and indexing methods. Among these methods, we can consider clustering and classification methods where the objective is to organize documents and to increase the speed of accessing to required information. In most of document clustering methods, the clustering is mostly executed based on word frequency and considering document as a bag of words. In this essay, in order to decrease the number of features and to choose basic document feature, we use background knowledge and word clustering methods. In fact by using WordNet ontology, background knowledge and clustering method, the similar words of documents are clustered and the clusters with the number of words more than threshold are chosen and then their frequency of words is accepted as the effective features of document. The results of this proposed method simulation shows that the documents dimensions are decreased effectively and consequently the performance of documents clustering is increased.

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Multilingual Single Document Keyword Extraction for Information Retrieval

Cited by 58 publications

References 7 publications

Keywords Similarity Based Topic Identification for Indonesian News Documents

Keywords Similarity Based Topic Identification for Indonesian News Documents

Keyword and Keyphrase Extraction Techniques: A Literature Review

Document features selection using background knowledge and word clustering technique

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