Mohammad Abdous


2016

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PersoNER: Persian Named-Entity Recognition
Hanieh Poostchi | Ehsan Zare Borzeshi | Mohammad Abdous | Massimo Piccardi
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network.