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2016 COLING COLING 2016

PersoNER: Persian Named-Entity Recognition

Abstract

AbstractNamed-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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Deep Learning and Machine Learning
🧭 Keyword Pioneer — persian language
🐣 Hot Topic Early Bird — persian language
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Hanieh Poostchi , Ehsan Zare Borzeshi , Mohammad Abdous , Massimo Piccardi

Topics

Artificial Intelligence > Learning Paradigms > Transfer Learning Machine Learning > Core Methods > Classification Deep Learning > Architectures > Neural Networks

Keywords

recurrent neural network word embedding persian language max-margin classifier named-entity recognition
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