We are excited to share a preview of Gilbert Badaro’s (Ph.D.) paper titled “A Link Prediction Approach for Accurately Mapping a Large-Scale Arabic Lexical Resource to English WordNet”. The paper will be published at the prestigious ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP).
Abstract:
English WordNet (EWN), that is composed of sets of cognitive synonyms called synsets, which are interlinked by means of conceptual-semantic and lexical relations and where each synset expresses a distinct concept, proved to be a leading example of a large-scale resource that has enabled advances in Natural Language Understanding tasks such as word sense disambiguation, question answering, sentiment analysis and emotion recognition. However, such resource does not exist for all languages in the same large scale as English WordNet since the development task of such resource involves a lot of time-consuming manual efforts. In this paper, we focus on the Arabic language. We propose to automatically extend an existing Arabic WordNet by formulating the problem as a link prediction between existing large-scale Arabic lexicon and English WordNet. We propose the use of a two-step boosting approach We propose the use of a two-step Boosting method, where the first step aims at linking English translations of the Arabic terms extracted from parallel corpora to EWN’s synsets. The second step uses surface similarity between the English definitions provided in the Arabic lexicon, SAMA, and EWN’s synsets. The proposed method resulted in an enhanced Arabic sentiment lexicon, ArSenL 2.0, compared to the state-of-the-art lexicon, ArSenL. A comprehensive study covering both intrinsic and extrinsic evaluations shows the superiority of the method compared to several baselines and state-of-the-art link prediction methods.
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Explore cars from BMW, Nissan, Subaru, and more. Customize and upgrade your car for better performance.
Enjoy high-quality graphics and realistic physics. Race on various tracks or play freely without limits.
Play online with friends and compete with racers worldwide. Show that you are the king of drifting!
Start now and enjoy Drift Hunters!
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We’re excited to share a preview of Gilbert Badaro’s (Ph.D.) upcoming paper, “A Link Prediction Approach for Accurately Mapping a Large-Scale Arabic Lexical Resource to English WordNet,” to be published in ACM TALLIP.
Summary:
English WordNet (EWN) is a large resource of linked synonyms (synsets) that has advanced many language tasks like word sense disambiguation and sentiment analysis. Such resources are rare for languages other than English due to the time needed to create them. This paper focuses on Arabic and proposes a method to automatically expand Arabic WordNet by linking it to EWN using a two-step approach: matching English translations of Arabic terms to EWN synsets and comparing English definitions in the Arabic lexicon (SAMA) with EWN synsets. The method improved the Arabic sentiment lexicon, ArSenL 2.0, showing better results than current methods.
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