Unveiling Hidden Intentions

Diaz, Gerardo Ocampo; Ng, Vincent

doi:10.1609/aaai.v34i09.7080

Cited by 2 publications

(3 citation statements)

References 25 publications

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“…More work needs to be done on the detection of intent in memes. Gerardo Ocampo Diaz et al [75] pointed out that capturing the hidden message and intention behind a meme is by itself a novel task that is challenged by the lack of a specific corpus with a suitable taxonomy of intent. Julia Kruk et al [76] proposed a multimodal data set based on Instagram posts, but so far, researchers have not been able to completely capture the 'complex meaning multiplication' mentioned in this work.…”

Section: Downstream Learning Tasks On Memesmentioning

confidence: 99%

Capturing the Concept Projection in Metaphorical Memes for Downstream Learning Tasks

Acharya,

Das,

Sudarshan

2024

IEEE Access

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Metaphorical memes, where a source concept is projected into a target concept, are an essential construct in figurative language. In this article, we present a novel approach for downstream learning tasks on metaphorical multimodal memes. Our proposed framework replaces traditional methods using metaphor annotations with a metaphor-capturing mechanism. Besides using the significant zero-shot learning capability of state-of-the-art pretrained encoders, this work introduces an alternative external knowledge enhancement strategy based on ChatGPT (chatbot generative pretrained transformer), demonstrating its effectiveness in bridging the intermodal semantic gap. We propose a new concept projection process consisting of three distinct components to capture the intramodal knowledge and intermodal concept gap in the forms of text modality embedding, visual modality embedding, and concept projection embedding. This approach leverages the attention mechanism of the Graph Attention Network for fusing the common aspects of external knowledge related to the knowledge in the text and image modality to implement the concept projection process. Our experimental results demonstrate the superiority of our proposed approach compared to existing methods.

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Section: Downstream Learning Tasks On Memesmentioning

confidence: 99%

Capturing the Concept Projection in Metaphorical Memes for Downstream Learning Tasks

Acharya,

Das,

Sudarshan

2024

IEEE Access

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“…There is enormous amount of data in social network and there in increasing interest to apply intention mining in social networks. The intention mining category dimension can be with six attributes: polarity intention, emotion intention, hidden intention, search intention, purchase intention, behavioural intention [34,35,36,37].…”

Section: B Intention Mining Categoriesmentioning

confidence: 99%

“…Hidden Intention Machine learning and Artificial intelligence are playing very crucial role in interpreting human intentions. Toady AI systems can read and see, but still they cannot identify the hidden intentions in a piece of text data or from certain actions performed by human [41]. The task of identifying hidden intentions is challenging.…”

Section: )mentioning

confidence: 99%

An Intensive Spectrum for Intention Mining Analysis

Varsha D. Jadhav

2023

IJRITCC

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There is huge volume of data in the social networks. This data can be retrieved and integrated to extract useful meaning and come out with the insights which is called as intentions. This can be used in different fields like business, recommender systems, education, Scientific research, games, etc. Also, there are various intention mining techniques which can be applied to several fields as information retrieval, business, etc. There is no specific definition of intention mining and also there is very less existing literature present. Accordingly, there is need to conduct systematic literature review of the very recent research area. Understanding intention mining, purpose of intention mining, categories and techniques of intention mining is the need. The paper endorses a spectrum for intention mining so that further literature review of intention mining can be completed. We validate our work through dimensions, categories and techniques for intention mining.

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Unveiling Hidden Intentions

Cited by 2 publications

References 25 publications

Capturing the Concept Projection in Metaphorical Memes for Downstream Learning Tasks

Capturing the Concept Projection in Metaphorical Memes for Downstream Learning Tasks

An Intensive Spectrum for Intention Mining Analysis

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