Development of Part of Speech Tagger Using Deep Learning

Singh*, Aarti; Verma, Charu; Seal, Swagata; Singh, Varsha

doi:10.35940/ijeat.a1531.109119

Cited by 3 publications

(2 citation statements)

References 44 publications

(44 reference statements)

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“…Unsupervised POS tagging models do not require pre-tagging, in contrast to supervised POS tagging models. Instead, they tag terms using automated techniques (Singh et al 2019). Tag sets, transformation rules, and other elements are automatically included in advanced computational techniques like the Baum-Welch algorithm.…”

Section: The Approaches To Pos Taggingmentioning

confidence: 99%

A Comprehensive Survey of Techniques Used for Part-of-Speech Tagging of Code-Mixed Social Media Text

Sunita,

Kumar,

Neetika

2023

Preprint

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Part-of-speech tagging faces unique difficulties when dealing with code-mixed social media text, which combines multiple languages in informal content created by users. In India, many web users employ a mixture of regional languages and English on platforms like Facebook, Instagram, and WhatsApp to express their messages and emotions. Text derived from social media is used in a variety of applications, such as speech recognition, machine learning, information retrieval, question answering, sentiment analysis, and named entity recognition. Due to training with monolingual texts, natural language processing tools such as part-of-speech taggers and parsers don't perform well. Assigning grammatical labels to individual words (such as verbs, adjectives, and nouns) is a critical task in natural language processing. This review paper extensively surveys the existing literature on part-of-speech tagging specifically developed for Indian and Foreign code-mixed social media text. We examine and categorize the approaches utilized in prior studies, taking into account the diverse techniques and methodologies employed to handle the complexities of code-mixed data. These approaches encompass rule-based methods, statistical models, and deep learning techniques such as recurrent neural networks and transformers. To enable comprehensive analysis, we compare and evaluate the performance of various state-of-the-art code-mixed part-of-speech taggers using benchmark datasets. We discuss the evaluation metrics used in these studies. Lastly, we explore the challenges introduced by noisy and informal language commonly found in code-mixed social media text. This review paper serves as a valuable resource for researchers and practitioners seeking to understand the current state of the art in code-mixed part-of-speech tagging for social media text. It offers insights into the strengths and weaknesses of existing approaches, identifies research gaps, and proposes potential avenues for future research to advance the field.

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Section: The Approaches To Pos Taggingmentioning

confidence: 99%

A Comprehensive Survey of Techniques Used for Part-of-Speech Tagging of Code-Mixed Social Media Text

Sunita,

Kumar,

Neetika

2023

Preprint

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“…In the beginning, randomly assigned weights are set at the beginning of algorithm training. Then, the MLP algorithm automatically performs weight changing to define the hidden layer unit representation is mostly good at minimizing the misclassification [54][55][56]. Besharati et al [54] proposed a POS tagging model for the Persian language using word vectors as the input for MLP and LSTM neural networks.…”

Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

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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