GRU based Named Entity Recognition System for Bangla Online Newspapers

Banik, Nayan; Rahman, Md. Hasan Hafizur

doi:10.1109/ciet.2018.8660795

Cited by 12 publications

(12 citation statements)

References 27 publications

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“…However, some researchers have used modern machine learning algorithms for the NER task. The machine learning algorithm that has been used in the NER is the RNN model [79]. In case of word sense disambiguation different machine learning techniques have been used like KNN-based algorithm, Pal et al [47], [80], Naive Bayes classifier and ANN [81].…”

Section: B Classical Methodsmentioning

confidence: 99%

Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning-Based Methods

et al. 2022

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The Bangla language is the seventh most spoken language, with 265 million native and non-native speakers worldwide. However, English is the predominant language for online resources and technical knowledge, journals, and documentation. Consequently, many Bangla-speaking people, who have limited command of English, face hurdles to utilize English resources. To bridge the gap between limited support and increasing demand, researchers conducted many experiments and developed valuable tools and techniques to create and process Bangla language materials. Many efforts are also ongoing to make it easy to use the Bangla language in the online and technical domains. There are some review papers to understand the past, previous, and future Bangla Natural Language Processing (BNLP) trends. The studies are mainly concentrated on the specific domains of BNLP, such as sentiment analysis, speech recognition, optical character recognition, and text summarization. There is an apparent scarcity of resources that contain a comprehensive review of the recent BNLP tools and methods. Therefore, in this paper, we present a thorough analysis of 75 BNLP research papers and categorize them into 11 categories,

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Section: B Classical Methodsmentioning

confidence: 99%

Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning-Based Methods

et al. 2022

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“…Also, the use of different Context-free Grammars are seen in the word sense disambiguation [46]. the NER is the RNN model [78]. In case of word sense disambiguation different machine learning techniques have been used like KNN-based algorithm, Pal et al [46], [79], Naive Bayes classifier and ANN [80].…”

Section: B Classical Methodsmentioning

confidence: 99%

“…Bangla natural language processing systems are essential for making the natural Bangla language understandable and interpretable to computers. BNLP focuses on clipping crucial features from the Bangla language speech and text data using available computing resources [78]. The modern world is generating a large amount of unstructured Bangla text and speech data every second.…”

Section: A Necessity and Needsmentioning

confidence: 99%

Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods

Sen,

Fuad,

Islam

et al. 2021

Preprint

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The Bangla language is the seventh most spoken language, with 265 million native and non-native speakers worldwide. However, English is the predominant language for online resources and technical knowledge, journals, and documentation. Consequently, many Bangla-speaking people, who have limited command of English, face hurdles to utilize English resources. To bridge the gap between limited support and increasing demand, researchers conducted many experiments and developed valuable tools and techniques to create and process Bangla language materials. Many efforts are also ongoing to make it easy to use the Bangla language in the online and technical domains. There are some review papers to understand the past, previous, and future Bangla Natural Language Processing (BNLP) trends. The studies are mainly concentrated on the specific domains of BNLP, such as sentiment analysis, speech recognition, optical character recognition, and text summarization. There is an apparent scarcity of resources that contain a comprehensive study of the recent BNLP tools and methods. Therefore, in this paper, we present a thorough review of 71 BNLP research papers and categorize them into 11 categories,

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“…Banik and Rahman et al [5] proposed a Gated Recurrent Unit (GRU), a variation of RNN, based named entity recognition for Bangla online newspapers with a manually annotated dataset. In this model, the sequence of RNN is represented by a fixed-sized vector which is fed to the recurrent unit one by one.…”

Section: Named Entity Recognition In Banglamentioning

confidence: 99%

Banner: A Cost-Sensitive Contextualized Model for Bangla Named Entity Recognition

Ashrafi¹,

Mohammad²,

Mauree³

et al. 2020

IEEE Access

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Named Entity Recognition (NER) is a task in Natural Language Processing (NLP) that aims to classify words into a predetermined list of Named Entities (NE). Many architectures have produced good results on high resourced languages like English and Chinese. However, the NER task has not yet achieved much progress for Bangla, a low resource Language. In this paper, we perform the NER task on Bangla Language using Word2Vec and contextual Bidirectional Encoder Representations from Transformers (BERT) embeddings. We propose multiple BERT-based deep learning models that use the contextualized embedding from BERT as inputs and a simple statistical approach for class weight cost sensitive learning. The modified cost-sensitive loss function was used to address the class imbalance of the data. In our modified cost-sensitive loss function, we penalize the dominant classes by taking the ratio concerning the maximum sample in a class instead of the whole dataset. This penalty is made so that the learner learns slowly for the dominant class. In addition, we experiment by adding a Conditional Random Field (CRF) layer and incorporating Focal Loss to the training process. We found the best F1 Macro score to be 65.96%, F1 Micro score of 90.64%, and F1 Message Understanding Coreference (MUC) score of 72.04%, which were calculated at Named Entity level. Our experimental results demonstrate that one of the proposed models, which jointly optimizes for the CRF loss and class weighted cost-sensitive loss according to our proposed statistical approach, achieve an improvement of over 8% F1 MUC score on a recently introduced Bangla NER dataset when compared to previously published work.

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GRU based Named Entity Recognition System for Bangla Online Newspapers

Cited by 12 publications

References 27 publications

Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning-Based Methods

Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning-Based Methods

Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods

Banner: A Cost-Sensitive Contextualized Model for Bangla Named Entity Recognition

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