Deep learning based parts of speech tagger for Bengali

Kabir, Md. Fasihul; Abdullah-Al-Mamun, Khandaker; Huda, Mohammad Nurul

doi:10.1109/iciev.2016.7760098

Cited by 27 publications

(2 citation statements)

References 7 publications

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“…However, using deep learning methodologies for POS tagging in languages with limited resources poses certain difficulties, primarily due to the limited availability of annotated datasets and the lack of experts during the development of these datasets. Despite these limitations, some studies have applied deep learning to POS tagging in languages such as Malayalam [33], Nepali [34], Bengali [35], Khasi [36], and Korean [37]. Further research in this area is ongoing.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Deep Learning Models for Part of Speech Tagging in the Malay Language

Adebayo,

Anbananthen,

Muthaiyah

et al. 2024

HighTech. Innov. J.

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Despite the widespread use of Malay, under-resourced languages like Malay face challenges in Natural Language Processing (NLP), particularly in Part-of-Speech (POS) tagging. The scarcity of annotated corpora poses a primary obstacle to POS tagging in Malay. This study aims to enhance the effectiveness and reliability of POS tagging models explicitly tailored for under-resourced languages within the field of NLP, focusing on Malay. Existing models, which rely on Conditional Random Fields and Hidden Markov Models, exhibit limitations, underscoring the need for more robust approaches. The research conducts a comparative analysis of various deep-learning models with different encoders for POS tagging in Malay sentences. The experimental analysis demonstrates that the Bidirectional Long Short-Term Memory (Bi-LSTM) model, leveraging a pre-trained Bidirectional Encoder Representations from Transformers (BERT) embedding model, achieves exceptional accuracy, precision, recall, and F1 scores in predicting tags. Notably, the BERT + Bi-LSTM model, boasting an accuracy of 98.82%, outperforms other models, showcasing superior performance across all evaluated metrics. Additionally, this combined model effectively handles known and unknown words, yielding highly accurate POS tagging results for Malay sentences. Doi: 10.28991/HIJ-2024-05-02-04 Full Text: PDF

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Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Deep Learning Models for Part of Speech Tagging in the Malay Language

Adebayo,

Anbananthen,

Muthaiyah

et al. 2024

HighTech. Innov. J.

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“…It helps us to decide whether to proceed with the upcoming search for further information [6]. For different kinds of tasks such as text classification [1], named entity recognition [2], parts of speech [3][4], sentiment analysis [5], and many other aspects of the text used Deep learning techniques. There is a subtle difference between keyword and keyphrase extraction.…”

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confidence: 99%

A Deep Learning Model Based on BERT and Sentence Transformer for Semantic Keyphrase Extraction on Big Social Data

et al. 2021

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In the evolution of the Internet, social media platform like Twitter has permitted the public user to share information such as famous current affairs, events, opinions, news, and experiences. Extracting and analyzing keyphrases in Twitter content is an essential and challenging task. Keyphrases can become precise the main contribution of Twitter content as well as it is a vital issue in vast Natural Language Processing (NLP) application. Extracting keyphrases is not only a time-consuming process but also requires much effort. The current works are on graph-based models or machine learning models. The performance of these models relies on feature extraction or statistical measures. In recent year, the application of deep learning algorithms to Twitter data have more insight due to automatic feature extraction can improve the performance of several tasks. This work aims to extract the keyphrase from Big social data using a sentence transformer with Bidirectional Encoder Representation Transformers (BERT) deep learning model. This BERT representation retains semantic and syntactic connectivity between tweets, enhancing performance in every NLP task on large data sets. It can automatically extract the most typical phrases in the Tweets. The proposed Semkey-BERT model shows that BERT with sentence transformer accuracy of 86% is higher than the other existing models.

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Bangla Part of Speech Tagging Using Contextual Embeddings and Oversampling Techniques

Roy

Hasan

Fuhad

et al. 2020

Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1

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Deep learning based parts of speech tagger for Bengali

Cited by 27 publications

References 7 publications

Comparative Analysis of Deep Learning Models for Part of Speech Tagging in the Malay Language

Comparative Analysis of Deep Learning Models for Part of Speech Tagging in the Malay Language

A Deep Learning Model Based on BERT and Sentence Transformer for Semantic Keyphrase Extraction on Big Social Data

Bangla Part of Speech Tagging Using Contextual Embeddings and Oversampling Techniques

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