Persian Named Entity Recognition

Dashtipour, K.; Gogate, M.; Adeel, A.; Algarafi, A.; Howard, N.; Hussain, A.

Abstract

Named Entity Recognition (NER) is an important natural language processing (NLP) tool for information extraction and retrieval from unstructured texts such as newspapers, blogs and emails. NER involves processing unstructured text for classification of words or expressions into relevant categories. In literature, NER has been developed for various languages but limited work has been conducted to develop NER for Persian text. This is due to limited resources (such as corpus, lexicons etc.) and tools for Persian named entities. In this paper, a novel scalable system for Persian Named Entity Recognition (PNER) is presented. The proposed PNER can recognize and extract three most important named entities in Persian script: the person name, location and date. The proposed PNER has been developed by combining a grammatical rule-based approach with machine learning. The proposed framework has integrated dictionaries of Persian named entities, Persian grammar rules and a Support Vector Machine (SVM). The performance evaluation of PNER in terms of precision, recall and f-measure has achieved comparable results with the state-of-the-art NER frameworks in other languages.

Citation

Dashtipour, K., Gogate, M., Adeel, A., Algarafi, A., Howard, N., & Hussain, A. (2017, July). Persian Named Entity Recognition. Presented at 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Oxford, UK

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)
• Start Date: Jul 26, 2017
• End Date: Jul 28, 2017
• Online Publication Date: Nov 16, 2017
• Publication Date: Nov 16, 2017
• Deposit Date: Sep 23, 2019
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 79-83
• DOI: https://doi.org/10.1109/ICCI-CC.2017.8109733
• Keywords: Named Entity Recognition, Persian , Sentiment Analysis, Affective computing, Support Vector Machines, Natural Language Processing
• Public URL: http://researchrepository.napier.ac.uk/Output/1792559