Towards Malay named entity recognition: an open-source dataset and a multi-task framework

Fu, Yingwen; Lin, Nankai; Yang, Zhihe; Jiang, Shengyi

doi:10.1080/09540091.2022.2159014

Cited by 4 publications

(1 citation statement)

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“…According to their experimental findings, the CRF [90], Bi-LSTM and CNN algorithms operate better together than the other basic techniques like Bi-LSTM, recurrent neural network (RNN) and long short-term (LSTM) alone. Fu et al [91] presented a technique for labelling datasets of related languages and iterative optimization to create a Malay NER dataset (MS-NER) which comprises 20,146 sentences. Furthermore, the authors suggested MTBR, a Multi-Task framework to incorporate boundary information to perform better for NER.…”

Section: (C) Deep Learning Approachmentioning

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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Section: (C) Deep Learning Approachmentioning

confidence: 99%