Part-of-Speech Tagger for Biomedical Domain Using Deep Neural Network Architecture

Gopalakrishnan, Athira; Soman, K P; Premjith, B.

doi:10.1109/icccnt45670.2019.8944559

Cited by 14 publications

(4 citation statements)

References 6 publications

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“…The first hidden layer processes the forward input sequences, while the other hidden layer processes it backward; both are then connected to the same output layer, which provides access to the future and past context of every point in the sequence. Hence BLSTM beat both standard LSTMs and RNNs, and it also significantly provides a faster and more accurate model [14,58].…”

Section: Bidirectional Long Short-term Memorymentioning

confidence: 98%

“…A Long Short-Term Memory (LSTM) is a special kind of RNN network architecture, which has the capability of learning long-term dependencies. An LSTM can also learn to fill the gap in time intervals in more than1000 steps [14,57,58].…”

Section: Long Short-term Memorymentioning

confidence: 99%

“…Gated recurrent unit (GRU) is an extension of recurrent neural network which aims to process memories of sequence of data by storing prior input state of the network, which they plan to target vectors based on the prior input [14,58].…”

Section: Gate Recurrent Unitmentioning

confidence: 99%

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Part of speech tagging: a systematic review of deep learning and machine learning approaches

2022

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Natural language processing (NLP) tools have sparked a great deal of interest due to rapid improvements in information and communications technologies. As a result, many different NLP tools are being produced. However, there are many challenges for developing efficient and effective NLP tools that accurately process natural languages. One such tool is part of speech (POS) tagging, which tags a particular sentence or words in a paragraph by looking at the context of the sentence/words inside the paragraph. Despite enormous efforts by researchers, POS tagging still faces challenges in improving accuracy while reducing false-positive rates and in tagging unknown words. Furthermore, the presence of ambiguity when tagging terms with different contextual meanings inside a sentence cannot be overlooked. Recently, Deep learning (DL) and Machine learning (ML)-based POS taggers are being implemented as potential solutions to efficiently identify words in a given sentence across a paragraph. This article first clarifies the concept of part of speech POS tagging. It then provides the broad categorization based on the famous ML and DL techniques employed in designing and implementing part of speech taggers. A comprehensive review of the latest POS tagging articles is provided by discussing the weakness and strengths of the proposed approaches. Then, recent trends and advancements of DL and ML-based part-of-speech-taggers are presented in terms of the proposed approaches deployed and their performance evaluation metrics. Using the limitations of the proposed approaches, we emphasized various research gaps and presented future recommendations for the research in advancing DL and ML-based POS tagging.

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Section: Bidirectional Long Short-term Memorymentioning

confidence: 98%

Section: Long Short-term Memorymentioning

confidence: 99%

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Part of speech tagging: a systematic review of deep learning and machine learning approaches

2022

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“…In terms of transformer models, the transfer learning ability improved the accuracy of many NLP applications [30,31]. The study by Gopalakrishnan et al [32] investigated the performance of LSTM and Gated Recurrent Unit (GRU) models on a biomedical dataset. The researchers found that the bi-directional versions of both LSTM and GRU models outperformed their respective simple models, showing superior results.…”

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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POS Tagger Model for South Indian Language Using a Deep Learning Approach

Shree

Shambhavi²

2022

Lecture Notes in Electrical Engineering

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Part-of-Speech Tagger for Biomedical Domain Using Deep Neural Network Architecture

Cited by 14 publications

References 6 publications

Part of speech tagging: a systematic review of deep learning and machine learning approaches

Part of speech tagging: a systematic review of deep learning and machine learning approaches

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

POS Tagger Model for South Indian Language Using a Deep Learning Approach

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