Towards POS Tagging Methods for Bengali Language: A Comparative Analysis

Jahara, Fatima; Barua, Adrita; Iqbal, Mohammad; Das, Avishek; Sharif, Omar; Hoque, Mohammed Moshiul; Sarker, Iqbal H.

doi:10.1007/978-3-030-68154-8_93

Cited by 4 publications

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“… where A denotes an agreement; M indicates the number of coding events agreed by all coders; denotes the number of coding events assigned by i th coder, and n represents the number of coders. Parts of speech analysis: in linguistic corpus, Parts of Speech (POS) Tagging, also known as grammatical tagging, is a process to label tokens with their lexical categories [ 68 ]. POS tagging is different in different languages.…”

Section: Metrics For Evaluationmentioning

confidence: 99%

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BEmoC: A Corpus for Identifying Emotion in Bengali Texts

et al. 2022

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Emotion classification in text has growing interest among NLP experts due to the enormous availability of people’s emotions and its emergence on various Web 2.0 applications/services. Emotion classification in the Bengali texts is also gradually being considered as an important task for sports, e-commerce, entertainments, and security applications. However, It is a very critical task to develop an automatic emotion classification system for low-resource languages such as, Bengali. Scarcity of resources and deficiency of benchmark corpora make the task more complicated. Thus, the development of a benchmark corpus is the prerequisite to develop an emotion classifier for Bengali texts. This paper describes the development of an emotional corpus (hereafter called ‘BEmoC’) for classifying six emotions in Bengali texts. The corpus development process consists of four key steps: data crawling, pre-processing, labelling, and verification. A total of 7000 texts are labelled into six basic emotion categories such as anger, fear, surprise, sadness, joy, and disgust, respectively. Dataset evaluation with 0.969 Cohen’s κ score indicates the close agreement between the corpus annotators and the expert. The analysis of evaluation also represents that the distribution of emotion words obeys Zipf’s law. Moreover, the results of BEmoC analysis shown in terms of coding reliability, emotion density, and most frequent emotion words, respectively.

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Section: Metrics For Evaluationmentioning

confidence: 99%

“…Parts of speech analysis: in linguistic corpus, Parts of Speech (POS) Tagging, also known as grammatical tagging, is a process to label tokens with their lexical categories [ 68 ]. POS tagging is different in different languages.…”

Section: Metrics For Evaluationmentioning

confidence: 99%

BEmoC: A Corpus for Identifying Emotion in Bengali Texts

et al. 2022

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NLP techniques for automating responses to customer queries: a systematic review

Olujimi

Ade-Ibijola

2023

Discov Artif Intell

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The demand for automated customer support approaches in customer-centric environments has increased significantly in the past few years. Natural Language Processing (NLP) advancement has enabled conversational AI to comprehend human language and respond to enquiries from customers automatically independent of the intervention of humans. Customers can now access prompt responses from NLP chatbots without interacting with human agents. This application has been implemented in numerous business sectors, including banking, manufacturing, education, law, and healthcare, among others. This study reviewed earlier studies on automating customer queries using NLP approaches. Using a systematic review methodology, 73 articles were analysed from reputable digital resources. The evaluated result offers an in-depth review of prior studies investigating the use of NLP techniques for automated customer service responses, including details on existing studies, benefits, and potential future study topics on the use of NLP techniques for business applications. The implications of the results were discussed and, recommendations made.

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