Psychological named entity recognition from psychological Arabic texts. (2017)

• Record Type: Journal Article
• Authors: Lakel, Kheira; Bendella, Fatima
• Is Part Of: International journal of metadata, semantics and ontologies. Volume 12:Number 2/3(2017)
• Page Start: 82
• Page End: 89
• Publication Date: 2017
• Subjects: NERA -- named entity recognition -- psychological sciences -- Arabic language -- Jape -- gazetteers -- GATE; Metadata -- Periodicals; Semantic Web -- Periodicals; Ontologies (Information retrieval) -- Periodicals; Data structures (Computer science) -- Periodicals; Information theory -- Periodicals; 005.74
• Languages: English
• ISSNs: 1744-2621

Abstract

The most important problems facing the Arabisation of modern science is the terminological inconsistency in translation; this problem becomes more complex in the medical field specifically in psychological sciences where the translation of English–Arabic medical terms poses real challenges for researchers eager to analyse and organise this information. Arabic NER (Named Entity Recognition) systems play a significant role in many areas of Natural Language Processing (NLP). In this paper, the problem of PsyNER (Psychological Named Entity Recognition) is tackled through integrating the rule-based and machine learning based approach to form a hybrid approach in attempt to enhance the overall performance of PsyNER. This system is capable to recognise eight types of named entities including mental disorders designated by the DSM-IV (Diagnostic and Statistical Manual of the American Psychiatric Association).