Bengali Named Entity Recognition: A survey with deep learning benchmark

Rifat, Jamiur Rahman; Abujar, Sheikh; Noori, Sheak Rashed Haider; Hossain, Syed Akhter

doi:10.1109/icccnt45670.2019.8944804

Cited by 3 publications

(9 citation statements)

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“…Similar discrepancies, which are noted above, are also observed in another publicly accessible Bangla NER dataset created by Rifat et al [25]. The authors labeled generic terms as the named entities, like 'country,' 'time,' etc., which resulted in wrong annotations.…”

Section: Related Worksupporting

confidence: 73%

“…Banik et al [44] used a Recurrent Neural Network (RNN) based approach for Bangla NER modeling. Later, Rifat et al [25] proposed a similar system with a combination of Bidirectional Gated Recurrent Unit (BGRU) and Convolutional Neural Network (CNN). Among recent studies, Karim et al used Densely Connected Network (DCN) in combination with Bidirectional Long Short Term Memory (BiLSTM).…”

Section: Related Workmentioning

confidence: 99%

“…Table 1 illustrates a comprehensive overview of recent works on Bangla NER. To avoid confusion and repetitiveness, the datasets of Karim et al [24] and Rifat et al [25] will be referred to as Dataset-K and Dataset-R in the later sections.…”

Section: Related Workmentioning

confidence: 99%

“…Previously published Bangla NER datasets suffer from several demerits: (1) while other languages have wellestablished studies to identify name entities like geopolitical, natural phenomena, artifacts, and events [23], none of them attempted to recognize those aforementioned entities in Bangla; and (2) previously produced datasets suffer from weak supervision as the annotation was done automatically by using automated tools developed for other popular online languages. For example, Karim et al [24], and Rifat et al [25] both prepared Bangla NER datasets that contain only four entity classes (Person, Location, Organization, Object [29]. Attempts to use such tools for Bangla NER resulted in erroneous systems, as the Bangla language neither belongs to any of the resource-rich language families nor the complex inflectional linguistic properties of Bangla were considered by any of those tools.…”

mentioning

confidence: 99%

“…Statistics of our proposed B-NER dataset comparing with publicly accessible Bangla NER dataset Dataset-K (Karim et al[24] ) and Dataset-R (Rifat et al[25]). …”

mentioning

confidence: 99%

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B-NER: A Novel Bangla Named Entity Recognition Dataset With Largest Entities and Its Baseline Evaluation

et al. 2023

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Within the Natural Language Processing (NLP) framework, Named Entity Recognition (NER) is regarded as the basis for extracting key information to understand texts in any language. As Bangla is a highly inflectional, morphologically rich, and resource-scarce language, building a balanced NER corpus with large and diverse entities is a demanding task. However, previously developed Bangla NER systems are limited to recognizing only three familiar entities: person, location, and organization. To address this significant limitation, we introduce a novel Bangla NER dataset B-NER, which was created using 22,144 manually annotated Bangla sentences collected from Bangla newspapers and Bangla Wikipedia. This dataset includes a total of 9,895 unique words which were manually categorized into eight different entity types, such as a person, organization, event, artifact, time indicator, natural phenomenon, geopolitical entity, and geographical location. Inter-annotator agreement experiments were conducted to validate the quality of annotations performed by three annotators, resulting in a Kappa score of 0.82. In this paper, we provide an outline of the annotation guideline illustrated with examples, discuss the B-NER dataset properties, and present benchmark evaluations of the dataset. To establish that B-NER is more comprehensive and balanced in comparison to other publicly accessible datasets, we conducted cross-dataset modeling and validation, i.e. trained NER model on one dataset while tested on another, and found that the model trained on B-NER performed the best in that settings. Furthermore, we performed exhaustive benchmark evaluations based on Bidirectional LSTM with fastText embeddings and sentence transformer models. Among these models, fine-tuned IndicBERT achieved noticeable results with a Macro-F1 of 86%. This dataset and baseline results will be publicly available under a CC-BY 4.0 license in the CoNLL-2002 format to facilitate further research on Bangla NER.

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Section: Related Worksupporting

confidence: 73%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

“…Statistics of our proposed B-NER dataset comparing with publicly accessible Bangla NER dataset Dataset-K (Karim et al[24] ) and Dataset-R (Rifat et al[25]). …”

mentioning

confidence: 99%

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B-NER: A Novel Bangla Named Entity Recognition Dataset With Largest Entities and Its Baseline Evaluation

et al. 2023

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Bangla-BERT: Transformer-Based Efficient Model for Transfer Learning and Language Understanding

et al. 2022

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The advent of pre-trained language models has directed a new era of Natural Language Processing (NLP), enabling us to create powerful language models. Among these models, Transformer-based models like BERT have grown in popularity due to their cutting-edge effectiveness. However, these models heavily rely on resource-intensive languages, forcing other languages into multilingual models(mBERT). The two fundamental challenges with mBERT become significantly more challenging in a resource-constrained language like Bangla. It was trained on a limited and organized dataset and contained weights for all other languages. Besides, current research on other languages suggests that a language-specific BERT model will exceed multilingual ones. This paper introduces Bangla-BERT a , a monolingual BERT model for the Bangla language. Despite the limited data available for NLP tasks in Bangla, we perform pre-training on the largest Bangla language model dataset, BanglaLM, which we constructed using 40 GB of text data. Bangla-BERT achieves the highest results in all datasets and vastly improves the state-of-the-art performance in binary linguistic classification, multilabel extraction, and named entity recognition, outperforming multilingual BERT and other previous research. The pre-trained model is assessed against several non-contextual models such as Bangla fasttext and word2vec the downstream tasks. Finally, this model is evaluated by transfer learning based on hybrid deep learning models such as LSTM, CNN, and CRF in NER, and it is observed that Bangla-BERT outperforms state-of-the-art methods. The proposed Bangla-BERT model is assessed by using benchmark datasets, including Banfakenews, Sentiment Analysis on Bengali News Comments, and Cross-lingual Sentiment Analysis in Bengali. Finally, it is concluded that Bangla-BERT surpasses all prior state-of-the-art results by 3.52%, 2.2%, and 5.3%. a https://huggingface.co/Kowsher/bangla-bert INDEX TERMS Bangla NLP, BERT-Base, Large Corpus,Transformer I. INTRODUCTION P RE-TRAINED language models based on the transformer architecture have become an absolute standard for state-of-the-art performance on a wide variety of natural language processing applications [1].BERT, a renowned transformer-based technique, brought a great revolution that had huge impacts on the evolution of NLP [2]. Since its release as an academic research paper, this technologically pioneering NLP model has amazed the AI world. It's the firstever deeply bidirectional and fully unsupervised technique

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A novel Data and Model Centric artificial intelligence based approach in developing high-performance Named Entity Recognition for Bengali Language

Lima,

Md Hasib,

Azam

et al. 2023

PLoS ONE

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Named Entity Recognition (NER) plays a significant role in enhancing the performance of all types of domain specific applications in Natural Language Processing (NLP). According to the type of application, the goal of NER is to identify target entities based on the context of other existing entities in a sentence. Numerous architectures have demonstrated good performance for high-resource languages such as English and Chinese NER. However, currently existing NER models for Bengali could not achieve reliable accuracy due to morphological richness of Bengali and limited availability of resources. This work integrates both Data and Model Centric AI concepts to achieve a state-of-the-art performance. A unique dataset was created for this study demonstrating the impact of a good quality dataset on accuracy. We proposed a method for developing a high quality NER dataset for any language. We have used our dataset to evaluate the performance of various Deep Learning models. A hybrid model performed with the exact match F1 score of 87.50%, partial match F1 score of 92.31%, and micro F1 score of 98.32%. Our proposed model reduces the need for feature engineering and utilizes minimal resources.

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Bengali Named Entity Recognition: A survey with deep learning benchmark

Cited by 3 publications

References 6 publications

B-NER: A Novel Bangla Named Entity Recognition Dataset With Largest Entities and Its Baseline Evaluation

B-NER: A Novel Bangla Named Entity Recognition Dataset With Largest Entities and Its Baseline Evaluation

Bangla-BERT: Transformer-Based Efficient Model for Transfer Learning and Language Understanding

A novel Data and Model Centric artificial intelligence based approach in developing high-performance Named Entity Recognition for Bengali Language

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