TITLE: Development of a Dataset and a Deep Learning Baseline Named Entity Recognizer for Three Low Resource Languages: Bhojpuri, Maithili and Magahi 

ABSTRACT: In Natural Language Processing (NLP) pipelines, Named Entity Recognition
(NER) is one of the preliminary problems, which marks proper nouns and other
named entities such as Location, Person, Organization, Disease etc. Such
entities, without a NER module, adversely affect the performance of a machine
translation system. NER helps in overcoming this problem by recognising and
handling such entities separately, although it can be useful in Information
Extraction systems also. Bhojpuri, Maithili and Magahi are low resource
languages, usually known as Purvanchal languages. This paper focuses on the
development of a NER benchmark dataset for the Machine Translation systems
developed to translate from these languages to Hindi by annotating parts of
their available corpora. Bhojpuri, Maithili and Magahi corpora of sizes 228373,
157468 and 56190 tokens, respectively, were annotated using 22 entity labels.
The annotation considers coarse-grained annotation labels followed by the
tagset used in one of the Hindi NER datasets. We also report a Deep Learning
based baseline that uses an LSTM-CNNs-CRF model. The lower baseline F1-scores
from the NER tool obtained by using Conditional Random Fields models are 96.73
for Bhojpuri, 93.33 for Maithili and 95.04 for Magahi. The Deep Learning-based
technique (LSTM-CNNs-CRF) achieved 96.25 for Bhojpuri, 93.33 for Maithili and
95.44 for Magahi.
In Natural Language Processing (NLP) pipelines, Named Entity Recognition
(NER) is one of the preliminary problems, which marks proper nouns and other
named entities such as Location, Person, Organization, Disease etc. Such
entities, without a NER module, adversely affect the performance of a machine
translation system. NER helps in overcoming this problem by recognising and
handling such entities separately, although it can be useful in Information
Extraction systems also. Bhojpuri, Maithili and Magahi are low resource
languages, usually known as Purvanchal languages. This paper focuses on the
development of a NER benchmark dataset for the Machine Translation systems
developed to translate from these languages to Hindi by annotating parts of
their available corpora. Bhojpuri, Maithili and Magahi corpora of sizes 228373,
157468 and 56190 tokens, respectively, were annotated using 22 entity labels.
The annotation considers coarse-grained annotation labels followed by the
tagset used in one of the Hindi NER datasets. We also report a Deep Learning
based baseline that uses an LSTM-CNNs-CRF model. The lower baseline F1-scores
from the NER tool obtained by using Conditional Random Fields models are 96.73
for Bhojpuri, 93.33 for Maithili and 95.04 for Magahi. The Deep Learning-based
technique (LSTM-CNNs-CRF) achieved 96.25 for Bhojpuri, 93.33 for Maithili and
95.44 for Magahi.
Development of a Dataset and a Deep Learning Baseline Named Entity Recognizer for Three Low Resource Languages: Bhojpuri, Maithili and Magahi
http://arxiv.org/abs/2009.06451v1