Abstract
This paper provides continuity with our previous work on the identification and classification of Arabic psychological verbs through lexicon-grammar tables. In this regard, we add transformational forms such as negation, passivization, and nominalization to enrich our lexicon grammar tables. However, these transformations link one sentence to another, keeping the same semantic material, such as أحبّ زيد ماري ‘Zaid loved Marie’ and its nominalized form أكنّ زيدٌ حبًّا لماري ‘Zaid has love for Marie’. The two sentences share the same predicate (أحبّ ‘to love’) and the same arguments (زيد ,ماري; ‘Zaid, Marie’), even though their structure is different. We also extend our previous tool of recognizing Arabic psychological verbs in sentences that allowed us to detect those verbs in texts and corpora by transforming their lexicon-grammar tables into NooJ dictionaries and syntactic grammars. In this context, we create transformational grammars to make this tool more powerful by detecting the Arabic psychological verbs in texts in all their transformational forms.
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Amzali, A., Mourchid, M., Mouloudi, A., Mbarki, S. (2021). Arabic Psychological Verb Recognition Through NooJ Transformational Grammars. In: Bekavac, B., Kocijan, K., Silberztein, M., Šojat, K. (eds) Formalising Natural Languages: Applications to Natural Language Processing and Digital Humanities. NooJ 2020. Communications in Computer and Information Science, vol 1389. Springer, Cham. https://doi.org/10.1007/978-3-030-70629-6_7
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