Combining Bag-of-Words and Bag-of-Concepts Representations for Arabic Text Classification

Alahmadi, Alaa; Joorabchi, Arash; Mahdi, Abdulhussain E.

doi:10.1049/cp.2014.0711

Cited by 11 publications

(16 citation statements)

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“…Elberrichi and Abidi (2012) selected the first synset from the list of synsets as a disambiguation method, arguing that the first one is the most accurate synset. Other work used all concepts, called the Bag of Concepts method (BoC), as a disambiguation method (Alahmadi et al, 2014;Elberrichi et al, 2008). Additionally, the concept in conjunction with the original term was considered to improve TC (Elberrichi et al, 2008).…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, the concept in conjunction with the original term was considered to improve TC (Elberrichi et al, 2008). As a result, using concepts to improve TC can be achieved using three distinct methods: (i) adding the concept to the original term; (ii) replacing the original word with the concept; (iii) using the list of concepts (BoC) only (Alahmadi et al, 2014;Elberrichi et al, 2008;Mansuy and Hilderman, 2006). The classification results of using concepts are improved by a ratio up to 7%.…”

Section: Related Workmentioning

confidence: 99%

“…ATCS can be used in several applications, such as web page and email filtering, automatic article indexing and clustering and natural language processing (Abouenour et al, 2008;Alkhalifa and Rodríguez, 2009;Boudabous et al, 2013;Elberrichi and Abidi, 2012). Because English is one of the dominant languages on the World Wide Web, in addition to some other European and Asian languages, most text classification systems are designed for categorizing documents written in one of these languages (Alahmadi et al, 2014;El-Halees, 2008). Few attempts have been made to develop an ATCS for documents written in other languages, including Arabic.…”

Section: Introductionmentioning

confidence: 99%

“…This is due to the lack of semantic information which is needed to improve text classification. As a result, there is an urgent need to develop ATCS which use semantic and conceptual approaches to classify Arabic documents (Alahmadi et al, 2014;Elberrichi and Abidi, 2012). Many tools are available to aide in creating semantic and concept-based ATCS for Arabic such as WordNet.…”

Section: Introductionmentioning

confidence: 99%

“…Some of the existing research have focused on the enrichment of the AWN itself to improve classification by either (i) extending the named entities (synsets) (Abouenour et al, 2008;Alkhalifa and Rodríguez, 2009;Elberrichi and Abidi, 2012) or (ii) enriching the relations already present in AWN (Boudabous et al, 2013). There are limited amount of research, however, that have tried to improve Text Classification (TC) processes using AWN components, such as using n-grams, synonym and concepts (Alahmadi et al, 2014;Elberrichi and Abidi, 2012). Many attempts have focused on using various classification algorithms to improve Arabic text classification (Al-Saleem, 2010;Bawaneh et al, 2008;El-Halees, 2008;Kanaan et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Enhancement of Arabic Text Classification Using Semantic Relations of Arabic WordNet

Yousif¹,

Samawi²,

Elkabani³

et al. 2015

Journal of Computer Science

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Arabic text classification methods have emerged as a natural result of the existence of a massive amount of varied textual information (written in Arabic language) on the web. In most text classification processes, feature selection is crucial task since it highly affects the classification accuracy. Generally, two types of features could be used: Statistical based features and semantic and concept features. The main interest of this paper is to specify the most effective semantic and concept features on Arabic text classification process. In this study, two novel features that use lexical, semantic and lexico-semantic relations of Arabic WordNet (AWN) ontology are suggested. The first feature set is List of Pertinent Synsets (LoPS), which is list of synsets that have a specific relation with the original terms. The second feature set is List of Pertinent Words (LoPW), which is list of words that have a specific relation with the original terms. Fifteen different relations (defined in AWN ontology) are used with both proposed features. Naïve Bayes classifier is used to perform the classification process. The experimental results, which are conducted on BBC Arabic dataset, show that using LoPS feature set improves the accuracy of Arabic text classification compared with the well-known Bag-of-Word feature and the recent Bag-of-Concept (synset) features. Also, it was found that LoPW (especially with related-to relation) improves the classification accuracy compared with LoPS, Bagof-Word and Bag-of-Concept.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%