Mahdi Mohseni


MorphoBERT: a Persian NER System with BERT and Morphological Analysis
Mahdi Mohseni | Amirhossein Tebbifakhr
Proceedings of the First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers


A Persian Part-Of-Speech Tagger Based on Morphological Analysis
Mahdi Mohseni | Behrouz Minaei-bidgoli
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper describes a method based on morphological analysis of words for a Persian Part-Of-Speech (POS) tagging system. This is a main part of a process for expanding a large Persian corpus called Peyekare (or Textual Corpus of Persian Language). Peykare is arranged into two parts: annotated and unannotated parts. We use the annotated part in order to create an automatic morphological analyzer, a main segment of the system. Morphosyntactic features of Persian words cause two problems: the number of tags is increased in the corpus (586 tags) and the form of the words is changed. This high number of tags debilitates any taggers to work efficiently. From other side the change of word forms reduces the frequency of words with the same lemma; and the number of words belonging to a specific tag reduces as well. This problem also has a bad effect on statistical taggers. The morphological analyzer by removing the problems helps the tagger to cover a large number of tags in the corpus. Using a Markov tagger the method is evaluated on the corpus. The experiments show the efficiency of the method in Persian POS tagging.