Arabic WordNet semantic relations enrichment through morpho-lexical patterns

Boudabous, Mohamed Mahdi; Kammoun, Nadège; Khedher, N.; Belguith, Lamia Hadrich; Sadat, Fatiha

doi:10.1109/iccspa.2013.6487245

Cited by 25 publications

(21 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancement of Arabic Text Classification Using Semantic Relations of Arabic WordNet

Yousif¹,

Samawi²,

Elkabani³

et al. 2015

Journal of Computer Science

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

“…It is widely used in Arabic natural language processing applications (Boudabous et al, 2013;Elberrichi and Abidi, 2012). AWN is composed of words (nouns, verbs, adjectives and adverbs), which are listed with their roots, their concepts (synsets) and relations among these concepts.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Arabic Text Classification Using Semantic Relations of Arabic WordNet

Yousif¹,

Samawi²,

Elkabani³

et al. 2015

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…For Arabic MT, the past 10 years have witnessed a lot of interest in translating from Arabic to English mostly due to large DARPA programs such as GALE and BOLT (Olive et al, 2011). There have been some limited efforts in comparison on translating into Arabic from English (Hamon and Choukri, 2011;Al-Haj and Lavie, 2012;El Kholy and Habash, 2012), but also between Arabic and other languages (Boudabous et al, 2013;Habash and Hu, 2009;Shilon et al, 2012;Cettolo et al, 2011). The JRC-Acquis collection, of which we translate a portion, is publicly available for research purposes and already exists in 22 languages (and others ongoing).…”

Section: Related Workmentioning

confidence: 99%

A Parallel Corpus for Evaluating Machine Translation between Arabic and European Languages

Habash¹,

Zalmout

Taji

et al. 2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 2

View full text Add to dashboard Cite

We present Arab-Acquis, a large publicly available dataset for evaluating machine translation between 22 European languages and Arabic. Arab-Acquis consists of over 12,000 sentences from the JRCAcquis (Acquis Communautaire) corpus translated twice by professional translators, once from English and once from French, and totaling over 600,000 words. The corpus follows previous data splits in the literature for tuning, development, and testing. We describe the corpus and how it was created. We also present the first benchmarking results on translating to and from Arabic for 22 European languages.

show abstract

“…Boudabous et al [19] attempted to improve the semantic relations already existing in Arabic WordNet (AWN) [47]. They use a linguistic method based on morpho-lexical patterns to extract semantic relations.…”

Section: 1mentioning

confidence: 99%

“…Major works in Arabic include the extraction of rhetorical relations [15] [16] and general semantic relations explicitly stated in a text [17], Wikipedia relations [18], synonyms [19], relations between named entities [20], verbs [21], spatial relations [22], grammatical relations [23], and ontological relations [24] [25]. The only study in our review that explicitly mentions antonymy as one of the extracted relations is [19], which uses morpho-lexical patterns applied on a set of Wikipedia articles as a corpus to enrich WordNet, but the results obtained for antonyms were 0%. Our study fills this gap in the field of automated antonym extraction for Arabic.…”

Section: Introductionmentioning

confidence: 99%

Ontological Lexicon Enrichment: The Badea System For Semi-Automated Extraction Of Antonymy Relations From Arabic Language Corpora

Al-Yahya

Al-Malak

Aldhubayi

2016

MJCS

View full text Add to dashboard Cite

Ontological lexicons are considered a rich source of knowledge for the development of various natural language processing tools and applications; however, they are expensive to build, maintain, and extend. In this paper, we present the Badea system for the semi-automated extraction of lexical relations, specifically antonyms using a pattern-based approach to support the task of ontological lexicon enrichment. The approach is based on an ontology of "seed" pairs of antonyms in the Arabic language; we identify patterns in which the pairs occur and then use the patterns identified to find new antonym pairs in an Arabic textual corpora. Experiments are conducted on Badea using texts from three Arabic textual corpuses: KSUCCA, KACSTAC, and CAC. The system is evaluated and the patterns' reliability and system performance is measured. The results from our experiments on the three Arabic corpora show that the pattern-based approach can be useful in the ontological enrichment task, as the evaluation of the system resulted in the ontology being updated with over 300 new antonym pairs, thereby enriching the lexicon and increasing its size by over 400%. Moreover, the results show important findings on the reliability of patterns in extracting antonyms for Arabic. The Badea system will facilitate the enrichment of ontological lexicons that can be very useful in any Arabic natural language processing system that requires semantic relation extraction.Keywords: Antonym Extraction, Ontology, Arabic Lexicon, Semantic Relation, Arabic NLP INTRODUCTIONA lexicon is defined as "the knowledge that a native speaker has about a language. This includes information about the form and meanings of words and phrases, lexical categorization, the appropriate usage of words and phrases, relationships between words and phrases, and categories of words and phrases" [1]. Lexicon is an essential element for natural language processing (NLP) applications. For some applications, such as machine translation, lexicon is a critical resource [2]. An important aspect of such lexicons that renders them effective, reusable, and sharable within the community is building them upon standards such as semantic Web standards in the form of ontologies. An ontological lexicon is a lexicon designed using an ontological model and developed as an ontology. Ontological lexicons play a vital role in NLP applications such as language analysis, semantic annotation, summarization, machine translation, sense disambiguation, generation of lexical-competence questions used in standard language tests, and other applications that rely on implicit information in the text.Although ontological lexicons provide a rich source of knowledge for NLP applications, like other types of computational lexicons, they are expensive to build, maintain, and extend [2] [3] [4]. Moreover, the task of relation extraction is essential in any ontological lexicon development. Relation extraction focuses on the extraction of structured relations from unstructured sources such as Web documents ...

show abstract

Arabic WordNet semantic relations enrichment through morpho-lexical patterns

Cited by 25 publications

References 1 publication

Enhancement of Arabic Text Classification Using Semantic Relations of Arabic WordNet

Enhancement of Arabic Text Classification Using Semantic Relations of Arabic WordNet

A Parallel Corpus for Evaluating Machine Translation between Arabic and European Languages

Ontological Lexicon Enrichment: The Badea System For Semi-Automated Extraction Of Antonymy Relations From Arabic Language Corpora

Contact Info

Product

Resources

About