Named Entity Recognition (NER) for Nepali

Maharjan, Gopal; Bal, Bal Krishna; Regmi, Santosh

doi:10.1007/978-3-030-29750-3_6

Cited by 3 publications

(3 citation statements)

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“…A Support Vector Machine (SVM) is a supervised machine learning technique utilized for both classification and regression tasks [27]. Its primary efficacy lies in binary classification scenarios where the objective is to categorize data points into two classes based on their inherent features [77].…”

Section: (D) Support Vector Machinementioning

confidence: 99%

“…• Support Vectors (SVs): These are data points nearest to the hyperplane that contribute to defining the margin. They are pivotal in influencing the hyperplane's positioning [27].…”

Section: (D) Support Vector Machinementioning

confidence: 99%

“…Based on Information retrieval systems require the contextual data that surrounds NEs where names perform an important role in information retrieval as significant information is always surrounded by NEs [27], [28]. Names have traditionally subscribed to the belief that automatic text summarization [29], biomedical data and text summarization systems can effectively use investigated NEs as they remain intact in all languages [4], [30], [31] NER models are typically trained using supervised machine learning techniques, where annotated training data is used to teach the model to recognize and classify named entities.…”

Section: Introductionmentioning

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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Section: (D) Support Vector Machinementioning

confidence: 99%

“…• Support Vectors (SVs): These are data points nearest to the hyperplane that contribute to defining the margin. They are pivotal in influencing the hyperplane's positioning [27].…”

Section: (D) Support Vector Machinementioning

confidence: 99%