Abstract
Often when we read some text, it leaves an impression in our mind. This perception imbibes the knowledge conveyed, the context, and the lexical information. Although there has been abundant research on the representation of text, research on devising techniques for visualization of embedded text is absent. Thus, we propose a novel “text-to-image” (TexIm) encoding enabling visualization of textual features. The proposed TexIm extracts the contextualized semantic and syntactic information present in the text through BERT and generates informed pictorial representations through a series of transformations. This unique representation is potent enough to assimilate the information conveyed, and the linguistic intricacies present in the text. Additionally, TexIm generates concise input representation that reduces the memory footprint by 37%. The proposed methodology has been evaluated on a hand-crafted dataset of Cricketer Biographies for the task of pair-wise comparison of texts. The conformity between the similarity of texts and the corresponding generated representations ascertain its fruitfulness.
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Ansar, W., Goswami, S., Chakrabarti, A., Chakraborty, B. (2023). TexIm: A Novel Text-to-Image Encoding Technique Using BERT. In: Tistarelli, M., Dubey, S.R., Singh, S.K., Jiang, X. (eds) Computer Vision and Machine Intelligence. Lecture Notes in Networks and Systems, vol 586. Springer, Singapore. https://doi.org/10.1007/978-981-19-7867-8_11
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