Named Entity Recognition of Kumauni Language using Machine Learning (ML)

Pant, Vinay Kumar; Sharma, Rupak; Kundu, Shakti

doi:10.21203/rs.3.rs-2424051/v1

Cited by 1 publication

(3 citation statements)

References 22 publications

(32 reference statements)

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“…(g) Machine Translation (MT): NER can assist in improving machine translation systems by correctly identifying and preserving named entities during the translation process [39]. This ensures that names of people, organizations and locations are accurately translated [4], [41], [47], [48].…”

Section: Application Of Nermentioning

confidence: 99%

“…The authors explored on NE recognition for Indian Languages reveals variations across different languages which is having more extensive work compared to others like Telugu, Marathi and Hindi. Pant et al [47] investigated the feasibility of developing a chatbot that can communicate in Kumaon language and possible difficulties that could come up during the method. Additionally, the authors also gave a thorough analysis of the language and the mapping of it into other languages to make the use of Kumaon's application in industrial processing which is easier to accessible.…”

Section: (D) Hybrid Approachmentioning

confidence: 99%

“…Litake et al [10] Hindi, Marathi IJCNLP 80% Mehta et al [102] Hindi, Indo-aryan Large Corpus 90.7% Saha et al [103] Hindi Test Corpus 73.87% Pant et al [47] Kumauni Annotated data Corpus 85% Krishnan et al [104] Malayalam Wikipedia, Google Graph F1=78% Liu et al [105] Urdu MULTIPLE, IDENTIFIER 80% Srivastava et al [106] Hindi IJCNLP-08 P=96.05, F=91.25%, R=86.90% Naz et al [107] Urdu IJCNLP and ACL corpus F=92.47%, R=95.57%, P=89.57% Ali et al [21] Sindhi IJCNLP-2008 82%…”

Section: Authors and References Languages Dataset Accuracymentioning

confidence: 99%

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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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