arXiv:2512.00088v2 Announce Type: replace-cross Abstract: We propose SemImage, a novel method for representing a text document as a two-dimensional semantic image to be processed by convolutional neural networks (CNNs). In a SemImage, each word is represented as a pixel in a 2D image: rows correspond to sentences and an additional boundary row is inserted between sentences to mark semantic transitions. Each pixel is not a typical RGB value but a vector in a disentangled HSV color space, encoding different linguistic features: the Hue with two components H_cos and H_sin to account for circulari
The continuous advancements in AI and natural language processing drive innovation in more efficient and novel data representations for machine learning models.
This development could significantly enhance the performance of text processing with CNNs and potentially lead to more disentangled and interpretable representations of linguistic features.
Traditional text embeddings are supplemented by a novel image-based representation, altering how large language models (LLMs) and other AI systems process and understand textual data.
- · AI researchers in NLP and computer vision
- · Developers of text analysis tools
- · Companies seeking more efficient data processing
- · Traditional text embedding methods if SemImage proves superior
- · Relying solely on sequential text processing
More accurate and nuanced understanding of textual data by AI systems.
Potential for new hybrid AI architectures combining vision and language models more effectively.
Accelerated development of multimodal AI applications that seamlessly integrate text and image processing.
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Read at arXiv cs.LG