A Zipf's law-based text generation approach for addressing imbalance in entity extraction

Wang, Zhenhua; Ren, Ming; Gao, Dong; Li, Zhuang

doi:10.1016/j.joi.2023.101453

Cited by 5 publications

(1 citation statement)

References 34 publications

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“…For the convenience of training, we process the train set to separate each content and its theme using "SEP," terminated by "EOS." We leverage the Bert Tokenizer to generate token embeddings from the processed train set (Wang et al, 2023), simultaneously forming position embeddings based on each token's location. Moreover, to distinguish between the Content and Theme, SEP is embedded, serving as segment embeddings.…”

Section: Mobile Data Economizing Expression Generatormentioning

confidence: 99%

Will sentiment analysis need subculture? A new data augmentation approach

Wang,

He,

et al. 2024

Asso for Info Science & Tech

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Nowadays, the omnipresence of the Internet has fostered a subculture that congregates around the contemporary milieu. The subculture artfully articulates the intricacies of human feelings by ardently pursuing the allure of novelty, a fact that cannot be disregarded in the sentiment analysis. This paper aims to enrich data through the lens of subculture, to address the insufficient training data faced by sentiment analysis. To this end, a new approach of subculture‐based data augmentation (SCDA) is proposed, which engenders enhanced texts for each training text by leveraging the creation of specific subcultural expression generators. The extensive experiments attest to the effectiveness and potential of SCDA. The results also shed light on the phenomenon that disparate subcultural expressions elicit varying degrees of sentiment stimulation. Moreover, an intriguing conjecture arises, suggesting the linear reversibility of certain subcultural expressions.

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Section: Mobile Data Economizing Expression Generatormentioning

confidence: 99%