A comparative analysis of transformer based models for figurative language classification

Junaid, Taha; Sumathi, D.; Sasikumar, A.N.; Suthir, S.; Manikandan, J.; Khilar, Rashmita; Kuppusamy, P.G.; Raju, M. Janardhana

doi:10.1016/j.compeleceng.2022.108051

Cited by 11 publications

(3 citation statements)

References 19 publications

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“…They proposed an improved variant where BERT is used as an encoder and decoder, and experimental results show that the model has high fluency and logical coherence in generating conversations. Junaid T et al [13] used self attention mechanism to capture contextual information of sentences. The Transformer model greatly accelerates training speed through parallel computation using self attention mechanism and achieves excellent performance in machine translation tasks.…”

Section: Literature Reviewmentioning

confidence: 99%

A Transformer-BERT Integrated Model-Based Automatic Conversation Method Under English Context

Li,

Liu,

Zhang

et al. 2024

IEEE Access

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The contextual understanding ability in complex conversation scenarios has been a challenging issue, and existing methods mostly failed to possess such characteristics. To bridge such gap, this paper formulates a novel composite large language model to investigate such issue. As a result, taking English context as the scene, a Transformer-BERT integrated model-based automatic conversation model is proposed in this work. Firstly, the unidirectional BERT-based automatic conversation model is improved by introducing attention mechanism. It is expected to enhance feature expression for conversation texts by linking context to identify long-difficult sentences. Besides, a bidirectional Transformer encoder is utilized as the input layer before the BERT encoder. Through the two modules, dynamic language training based on English situational conversations can be completed to build the automatic conversation model. The proposed conversation model is further assessed on massive real-world English language context in terms of conversation performance. The experimental results show that compared with traditional rule-based or machine learning methods, the proposal has significantly improved response quality and fluency in English context. It can more accurately understand context, capture subtle semantic differences, and generate more coherent responses.

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Section: Literature Reviewmentioning

confidence: 99%

A Transformer-BERT Integrated Model-Based Automatic Conversation Method Under English Context

Li,

Liu,

Zhang

et al. 2024

IEEE Access

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“…Text data mining refers to the discovery of patterns as proposed by Fayyad et al 10 , while natural language processing (NLP) is seen as a branch of artificial intelligence that deals with human language 17 or makes this language understandable to computers 18 thereby enabling different approaches, including the grouping of texts by topics (“topic modeling”). Topics are groups of similar objects, being a particular case of clustering.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised natural language processing in the identification of patients with suspected COVID-19 infection

Silva,

Pollettini,

Pazin Filho

2023

Cad. Saúde Pública

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Patients with post-COVID-19 syndrome benefit from health promotion programs. Their rapid identification is important for the cost-effective use of these programs. Traditional identification techniques perform poorly especially in pandemics. A descriptive observational study was carried out using 105,008 prior authorizations paid by a private health care provider with the application of an unsupervised natural language processing method by topic modeling to identify patients suspected of being infected by COVID-19. A total of 6 models were generated: 3 using the BERTopic algorithm and 3 Word2Vec models. The BERTopic model automatically creates disease groups. In the Word2Vec model, manual analysis of the first 100 cases of each topic was necessary to define the topics related to COVID-19. The BERTopic model with more than 1,000 authorizations per topic without word treatment selected more severe patients - average cost per prior authorizations paid of BRL 10,206 and total expenditure of BRL 20.3 million (5.4%) in 1,987 prior authorizations (1.9%). It had 70% accuracy compared to human analysis and 20% of cases with potential interest, all subject to analysis for inclusion in a health promotion program. It had an important loss of cases when compared to the traditional research model with structured language and identified other groups of diseases - orthopedic, mental and cancer. The BERTopic model served as an exploratory method to be used in case labeling and subsequent application in supervised models. The automatic identification of other diseases raises ethical questions about the treatment of health information by machine learning.

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“…Information security is the set of forms that secure data faraway from illegal connection, revelation, duplication, alteration. It is intended to preserve in different ways, Confidentiality (c), Integrity (I), and Availability (A) of trade information (Andress, 2014;Junaid et al, 2022). As such, data security systems are developed around the center destinations of CIA triad is guaranteeing private data is just revealed to approved people (C), forestalling unapproved alterations to private data (I), and ensuring that data is accessed when requested by approved individuals (A) (Ahmad et al, 2014;Thaler et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Cognitive Intelligence for Interrogation and Inflation of Information Security: A Survey

Jagannathan

Parvees²

2022

JART

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Information security is the set of forms that secure data faraway from illegal connection, revelation, duplication, alteration. As of late, increasingly legitimate frameworks such as resourceful inter and intra urban city, remote sensor systems, authentication digital signature frameworks, and superintendence, have recently needed data security affirmations. Hence, over the past researches numerous diverse strategies focused on cognitive intelligence are created for data security. In any case, there are not complete reviews which recapitulate strategies. In this way, this document surveyed on cognitive intelligence move toward presented on publications and symposiums for data security within most recent years. This article gives a precise review to audit various cognitive intelligence access and applications of data security. In additionally, deliberate the threat of cognitive intelligence access and applications for data security is conferred.

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A comparative analysis of transformer based models for figurative language classification

Cited by 11 publications

References 19 publications

A Transformer-BERT Integrated Model-Based Automatic Conversation Method Under English Context

A Transformer-BERT Integrated Model-Based Automatic Conversation Method Under English Context

Unsupervised natural language processing in the identification of patients with suspected COVID-19 infection

Cognitive Intelligence for Interrogation and Inflation of Information Security: A Survey

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