سیستم شناسایی و طبقه‌بندی موجودیت‌های اسمی در متون زبان فارسی بر پایه شبکه عصبی

Named Entity Recognition (NER) is a fundamental task in natural language processing and also known as a subset of information extraction. We seek to locate and classify named entities in text into predefined categories such as the names of persons, organizations, locations, expressions of times, etc. Named Entity Recognition for English texts has been researched widely for the past years, however only a few limited researches have emphasized on Persian NER due to the absence of resources for Persian named entities and the limited amount of progress made in Persian natural language processing in general. In this paper, a Persian named entity recognition system has been developed based on neural network with the study of researches conducted in other languages and benefiting from the latest methods in this area such as using the vector representation of words. The results from the proposed model show that word embedding features in Persian not only resolve the problem of feature selection, but also it could lead to the development of an efficient system with the least dependence to the domain.