An Ontology-based Summarization System for Arabic Documents (OSSAD)

Imam, Ibrahim F.; Nounou, Nihal; Hamouda, Alaa; Khalek, Hebat Allah Abdul

doi:10.5120/12980-0237

Cited by 19 publications

(13 citation statements)

References 11 publications

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“…A word in a sentence can be expanded by relating it to other words in a sentence to determine the actual meaning of the word. This is essential because the majority of research studies to date, have investigated opinion words [20], [21] or examined text mining through the creation of summaries of a given document [22], [23] in different languages such as Arabic [24], [24] and Chinese [25], [26]. This is so that a document or sentence can easily be understood by users as well as by intelligent machines.…”

Section: Related Workmentioning

confidence: 99%

Semi Supervised Method for Detection of Ambiguous Word and Creation of Sense: Using WordNet

Saqib¹,

Kundi²,

Syed³

et al. 2018

ijacsa

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Machine Translation, Information Retrieval and Knowledge Acquisition are the three main applications of Word Sense Disambiguation (WSD). The sense of a target word can be identified from a dictionary using a 'bag of words', i.e. neighbours of the target word. A target word has the same spelling of the word but with a different meaning, i.e. chair, light etc. In WSD, the key input sources are sentences and target words. But, instead of providing a target word, this should automatically be detected. If a sentence has more than one target word, then the filtration process will require further processing. In this study, the proposed framework, consisting of buzz words and query words has been developed to detect target words using the WordNet dictionary. Buzz words are defined as a 'bag-ofwords' using POS-Tags, and query words are those words having multiple meanings. The proposed framework will endeavor to find the sense of the detected target word using its gloss and with examples containing buzz words. This is a semi-supervised approach because 266 words of multiple meanings have been labelled from various sources and used based on an unsupervised approach to detect the target word and sense (meaning). After experimenting on a dataset consisting of 300 hotel reviews, 100 % of the target words for each sentence were detected with 84 % related to the sense of each sentence or phrase.

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Section: Related Workmentioning

confidence: 99%

Semi Supervised Method for Detection of Ambiguous Word and Creation of Sense: Using WordNet

Saqib¹,

Kundi²,

Syed³

et al. 2018

ijacsa

View full text Add to dashboard Cite

show abstract

“…[2]. In this massive amount of information, it is very difficult to choose the relevant information that meets the needs of the user [3]. The search engine is required to perform a significant task to provide the user with a subset of the original data.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Latent Semantic Analysis Approach for Arabic Document Summarization

et al. 2018

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The fast-growing amount of information on the Internet makes the research in automatic document summarization very urgent. It is an effective solution for information overload. Many approaches have been proposed based on different strategies, such as latent semantic analysis (LSA). However, LSA, when applied to document summarization, has some limitations which diminish its performance. In this work, we try to overcome these limitations by applying statistic and linear algebraic approaches combined with syntactic and semantic processing of text. First, the part of speech tagger is utilized to reduce the dimension of LSA. Then, the weight of the term in four adjacent sentences is added to the weighting schemes while calculating the input matrix to take into account the word order and the syntactic relations. In addition, a new LSA-based sentence selection algorithm is proposed, in which the term description is combined with sentence description for each topic which in turn makes the generated summary more informative and diverse. To ensure the effectiveness of the proposed LSA-based sentence selection algorithm, extensive experiment on Arabic and English are done. Four datasets are used to evaluate the new model, Linguistic Data Consortium (LDC) Arabic Newswire-a corpus, Essex Arabic Summaries Corpus (EASC), DUC2002, and Multilingual MSS 2015 dataset. Experimental results on the four datasets show the effectiveness of the proposed model on Arabic and English datasets. It performs comprehensively better compared to the state-of-the-art methods.

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“…Imam et al [25] described an approach for Arabic summarization using a domain ontology and extraction of ontology relations that are explicitly stated in the text: taxonomic and semantics. Their approach is evaluated against other summarization systems and against human experts.…”

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%.…”

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

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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 ...

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An Ontology-based Summarization System for Arabic Documents (OSSAD)

Cited by 19 publications

References 11 publications

Semi Supervised Method for Detection of Ambiguous Word and Creation of Sense: Using WordNet

Semi Supervised Method for Detection of Ambiguous Word and Creation of Sense: Using WordNet

An Enhanced Latent Semantic Analysis Approach for Arabic Document Summarization

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

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