ORIGINAL ARTICLE
FNLP-ONT: A feasible ontology for improving NLP tasks in Persian
Zahra Hosseini Pozveh,
Amirhassan Monadjemi,
Ali Ahmadi,
Zahra Hosseini Pozveh
Department of Computer, Science and Research branch, Islamic Azad University, Tehran, Iran
Search for more papers by this authorCorresponding Author
Amirhassan Monadjemi
Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran
Correspondence
Amirhassan Monadjemi, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran.
Email: [email protected]
Search for more papers by this authorAli Ahmadi
School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
Faculty of Computer Engineering, K.N.Toosi University of Technology, Tehran, Iran
Search for more papers by this authorZahra Hosseini Pozveh,
Amirhassan Monadjemi,
Ali Ahmadi,
Zahra Hosseini Pozveh
Department of Computer, Science and Research branch, Islamic Azad University, Tehran, Iran
Search for more papers by this authorCorresponding Author
Amirhassan Monadjemi
Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran
Correspondence
Amirhassan Monadjemi, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran.
Email: [email protected]
Search for more papers by this authorAli Ahmadi
School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
Faculty of Computer Engineering, K.N.Toosi University of Technology, Tehran, Iran
Search for more papers by this authorAbstract
Natural language processing is a composition of several error-prone and challenging tasks, including part of speech tagging, word sense disambiguation, named entity recognition, and compound verb detection. Studying intrasentence relations and roles is essential to improve the mentioned subtasks. Semi-automatic schemes such as ontologies can be applied to clarify word's dependencies. This paper presents an ontology that is targeting to improve POS tagging, WSD, NER, and compound verb detection in Persian with extra properties that may ameliorate machine translation. The ontology is tested in combinations with several state-of-art algorithms on Dadegan corpus. The results show that coping semantic analysis with machine learning methods enhance relation detection and consequently precision of the mentioned subtasks, which is not widely addressed in Persian. Furthermore, the experimental results declare that the accuracy rate increases between 4.5 and 23% for different tasks.
CONFLICTS OF INTEREST
None.
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