Mono Versus Multilingual BERT: A Case Study in Hindi and Marathi Named Entity Recognition

Litake, Onkar; Sabane, Maithili; Patil, Parth; Ranade, Aparna; Joshi, Raviraj

doi:10.1007/978-981-19-6088-8_56

Cited by 13 publications

(10 citation statements)

References 22 publications

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“…In line with [9], we evaluated various NER models using standard performance metrics, including Accuracy and F1-score. We assessed the performance at both the class level and overall performance.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Can LLM Substitute Human Labeling? A Case Study of Fine-grained Chinese Address Entity Recognition Dataset for UAV Delivery

Yao,

Luo,

Zhao

et al. 2024

Companion Proceedings of the ACM Web Conference 2024

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We present CNER-UAV, a fine-grained Chinese Name Entity Recognition dataset specifically designed for the task of address resolution in Unmanned Aerial Vehicle delivery systems. The dataset encompasses a diverse range of five categories, enabling comprehensive training and evaluation of NER models. To construct this dataset, we sourced the data from a real-world UAV delivery system and conducted a rigorous data cleaning and desensitization process to ensure privacy and data integrity. The resulting dataset, consisting of around 12,000 annotated samples, underwent human experts and Large Language Model annotation. We evaluated classical NER models on our dataset and provided in-depth analysis. The dataset and models are publicly available at https: //github.com/zhhvvv/CNER-UAV.

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Section: Evaluation Metricsmentioning

confidence: 99%

Can LLM Substitute Human Labeling? A Case Study of Fine-grained Chinese Address Entity Recognition Dataset for UAV Delivery

Yao,

Luo,

Zhao

et al. 2024

Companion Proceedings of the ACM Web Conference 2024

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“…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%

“…They deleted the IOB formatting from the IJCNLP 200 NER Corpus in accordance with previous work on the corpus. It also has mainly three dataset such as IJCNLP 200 NER [10], IJCNLP-2008 [21], [78] and IJCNLP-08 [93].…”

Section: Datasetmentioning

confidence: 99%

“…NLP algorithms and models are designed to handle the complexities of language [1], [4], [6], [7], [8], [9]. NER stands for Name Entity Recognition, is a task of NLP that involves identifying and classifying named entities, like names of person, location, organization and data [3], [5], [10], [11], [12], [13], [14], [15]. It helps machines to extract meaningful information from unstructured text data.…”

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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“…Due to the state's geographic and demographic expanse, Marathi has evolved into a language with multiple varieties and dialects. Recently, there has been some focus on Marathi NLP based on the Devanagari script (Joshi, 2022b,a;Kulkarni et al, 2021;Litake et al, 2022). However, a large chunk of tweets, posts, and comments in Marathi are in codemixed form.…”

Section: Introductionmentioning

confidence: 99%

My Boli: Code-mixed Marathi-English Corpora, Pretrained Language Models and Evaluation Benchmarks

Chavan,

Gokhale,

Kane

et al. 2023

Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings)

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The research on code-mixed data is limited due to the unavailability of dedicated code-mixed datasets and pre-trained language models. In this work, we focus on the low-resource Indian language Marathi which lacks any prior work in code-mixing. We present L3Cube-MeCorpus, a large code-mixed Marathi-English (Mr-En) corpus with 10 million social media sentences for pretraining. We also release L3Cube-MeBERT and MeRoBERTa, code-mixed BERT-based transformer models pre-trained on MeCorpus. Furthermore, for benchmarking, we present three supervised datasets MeHate, MeSent, and MeLID for downstream tasks like codemixed Mr-En hate speech detection, sentiment analysis, and language identification respectively. These evaluation datasets individually consist of manually annotated ~12,000 Marathi-English code-mixed tweets. Ablations show that the models trained on this novel corpus significantly outperform the existing state-of-the-art BERT models. This is the first work that presents artifacts for code-mixed Marathi research. All datasets and models are publicly released at https://github.com/ l3cube-pune/MarathiNLP.

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Mono Versus Multilingual BERT: A Case Study in Hindi and Marathi Named Entity Recognition

Cited by 13 publications

References 22 publications

Can LLM Substitute Human Labeling? A Case Study of Fine-grained Chinese Address Entity Recognition Dataset for UAV Delivery

Can LLM Substitute Human Labeling? A Case Study of Fine-grained Chinese Address Entity Recognition Dataset for UAV Delivery

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

My Boli: Code-mixed Marathi-English Corpora, Pretrained Language Models and Evaluation Benchmarks

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