Named Entity Recognition: A Survey for Indian Languages

Bhattacharjee, Krishnanjan; S, Shiva Karthik; Mehta, Swati; Kumar, Ajai; Mehta, Ria; Pandya, Dweep; Chaudhari, Pratik; Verma, Devika

doi:10.1109/icicict46008.2019.8993236

Cited by 9 publications

(3 citation statements)

References 13 publications

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“…Furthermore, the lack of a well-ordered labeled dataset makes advanced approaches such as deep learning methods difficult to deploy. [4] has described various problems faced while implementing NER for Indian languages.…”

Section: Related Workmentioning

confidence: 99%

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L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models

Patil¹,

Ranade²,

Sabane³

et al. 2022

Preprint

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Named Entity Recognition (NER) is a basic NLP task and finds major applications in conversational and search systems. It helps us identify key entities in a sentence used for the downstream application. NER or similar slot filling systems for popular languages have been heavily used in commercial applications. In this work, we focus on Marathi, an Indian language, spoken prominently by the people of Maharashtra state. Marathi is a low resource language and still lacks useful NER resources. We present L3Cube-MahaNER, the first major gold standard named entity recognition dataset in Marathi. We also describe the manual annotation guidelines followed during the process. In the end, we benchmark the dataset on different CNN, LSTM, and Transformer based models like mBERT, XLM-RoBERTa, IndicBERT, MahaBERT, etc. The MahaBERT provides the best performance among all the models. The data and models are available at https://github.com/l3cubepune/MarathiNLP

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Section: Related Workmentioning

confidence: 99%

“…We experiment on all major multi-lingual and Marathi BERT models to establish a benchmark for future comparisons. The Ma-haBERT model model fined-tuned on L3Cube-MahaNER is termed as MahaNER-BERT 4 and is shared publicly on model hub. All the resources are publicly shared on github 5 .…”

Section: Introductionmentioning

confidence: 99%

L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models

Patil¹,

Ranade²,

Sabane³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…It is used in various applications such as multilingual landscape of India, information retrieval, Search engines, Machine translation, question answering system and data extraction etc. [4], [16], [17], [18]. Due to its usefulness across a diverse range of domains and applications, NER has become a core activity.…”

Section: Introductionmentioning

confidence: 99%

State-of-art approach for Indian Language based on NER: Comprehensive Review

Pandey,

Nathani

2024

Preprint

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Named Entity Recognition (NER) is a fundamental task of natural language processing (NLP) that focuses on the identification and classification of named entities such as name of individual persons, location, organization and dates within the text. NER plays a pivotal role in various NLP applications, including information extraction, question answering, text summarization and sentiment analysis. Natural language processing's (NLPs) fundamental issue is named entity recognition (NER). While extensive research has been conducted on NER for English and Hindi, the complexities of Indian languages present unique challenges that require customized solutions. Working with NER for Indian languages is a difficult endeavor with limited resources available. This article provides a comprehensive review of NER approaches tailored for Indian languages. Indian languages pose unique challenges to NER due to their rich morphological and syntactic variations, script diversity and limited annotated data availability. This paper reviews the various techniques and methodologies employed in NER for Indian languages, including rule-based, machine learning and deep learning approaches. It analyzes the strengths and limitations of each approach. Additionally, this article examines the recent advancements in transfer learning and multilingual models, showcasing their potential in improving NER performance across Indian languages. This paper aims to guide researchers and practitioners in the development of NER systems for Indian languages and foster further advancements in this field. This article also provides a comprehensive review of the diverse approaches employed for NER in Indian languages, highlighting the strength and limitations as well.

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