  The goal in the NER task is to classify proper nouns of a text into classes
such as person, location, and organization. This is an important preprocessing
step in many NLP tasks such as question-answering and summarization. Although
many research studies have been conducted in this area in English and the
state-of-the-art NER systems have reached performances of higher than 90
percent in terms of F1 measure, there are very few research studies for this
task in Persian. One of the main important causes of this may be the lack of a
standard Persian NER dataset to train and test NER systems. In this research we
create a standard, big-enough tagged Persian NER dataset which will be
distributed for free for research purposes. In order to construct such a
standard dataset, we studied standard NER datasets which are constructed for
English researches and found out that almost all of these datasets are
constructed using news texts. So we collected documents from ten news websites.
Later, in order to provide annotators with some guidelines to tag these
documents, after studying guidelines used for constructing CoNLL and MUC
standard English datasets, we set our own guidelines considering the Persian
linguistic rules.
