PersoNER: Persian named-entity recognition

Poostchi, H; Borzeshi, EZ; Abdous, M; Piccardi, M

PersoNER: Persian named-entity recognition

Poostchi, H

Borzeshi, EZ Abdous, M Piccardi, M

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Publication Type:: Conference Proceeding
Citation:: COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers, 2016, pp. 3381 - 3389
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Poostchi, H https://orcid.org/0000-0002-8725-2619	en_US
dc.contributor.author	Borzeshi, EZ	en_US
dc.contributor.author	Abdous, M	en_US
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers, 2016, pp. 3381 - 3389	en_US
dc.identifier.isbn	9784879747020	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121930
dc.description.abstract	© 1963-2018 ACL. 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.	en_US
dc.relation.ispartof	COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers	en_US
dc.title	PersoNER: Persian named-entity recognition	en_US
dc.type	Conference Proceeding
utslib.for	080104 Computer Vision	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 1963-2018 ACL. 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.

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http://hdl.handle.net/10453/80094