Annotated Corpus with Negation and Speculation in Arabic Review Domain: NSAR
Mahany, Ahmed and Khaled, Heba and Nouh, Elmitwally and Aljohani, Naif and Ghoniemy, Said (2022) Annotated Corpus with Negation and Speculation in Arabic Review Domain: NSAR. International Journal of Advanced Computer Science and Applications, 13 (7). ISSN 2158-107X
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Abstract
Negation and speculation detection are critical for Natural Language Processing (NLP) tasks, such as sentiment analysis, information retrieval, and machine translation. This paper presents the first Arabic corpus in the review domain annotated with negation and speculation. The Negation and Speculation Arabic Review (NSAR) corpus consists of 3K randomly selected review sentences from three well-known and benchmarked Arabic corpora. It contains reviews from different categories, including books, hotels, restaurants, and other products written in various Arabic dialects. The negation and speculation keywords have been annotated along with their linguistic scope based on the annotation guidelines reviewed by an expert linguist. The inter-annotator agreement between two independent annotators, Arabic native speakers, is measured using the Cohen’s Kappa coefficients with values of 95 and 80 for negation and speculation, respectively. Furthermore, 29% of this corpus includes at least one negation instance, while only 4% of this corpus contains speculative content. Therefore, the Arabic reviews focus more on negation structures rather than speculation. This corpus will be available for the Arabic research community to handle these critical phenomena.
| Item Type: | Article | ||||||
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| Identification Number: | https://doi.org/10.14569/IJACSA.2022.0130706 | ||||||
| Dates: |
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| Uncontrolled Keywords: | Arabic NLP; negation; speculation; uncertainty; annotation; annotation guidelines; corpus; review domain; sentiment analysis | ||||||
| Subjects: | CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence |
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| Divisions: | Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology | ||||||
| Depositing User: | Nouh Elmitwally | ||||||
| Date Deposited: | 31 Aug 2022 14:46 | ||||||
| Last Modified: | 31 Aug 2022 14:46 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/13503 |
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