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    政大機構典藏 > 商學院 > 資訊管理學系 > 會議論文 >  Item 140.119/156149


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    題名: Exploring the Semantic Representations of Text in Subspaces of Latent Space: A Case Study on Color
    作者: 蕭舜文
    Hsiao, Shun-Wen;Lo, Yung-Fu
    貢獻者: 資管系
    關鍵詞: Latent Space;Semantic Representation;Concept Subspace;Projection Optimization;NLP
    日期: 2024-12
    上傳時間: 2025-03-12 10:22:06 (UTC+8)
    摘要: Language models like BERT have advanced the representation of textual semantics in high-dimensional latent spaces, enabling numerous natural language processing applications. However, their capacity to represent domain-specific concepts, such as "color," remains underexplored. This study investigated the semantic representation of text in color concept subspace of latent space. Using embeddings of nearly 1,000 color names from the XKCD color survey generated by BERT, we identified limitations in BERT’s ability to cluster perceptually similar colors. To address this, we proposed a supervised learning approach to project embeddings into a color-specific subspace, isolating and enhancing color semantics. Experimental results demonstrated the methodology’s effectiveness in improving semantic clustering through qualitative and quantitative evaluations. Moreover, our general approach not only explored the concept of color but also provided the possibility of exploring and disentangling semantic subspaces for other domain-specific concepts, contributing to the understanding and manipulation of latent space structures in language models.
    關聯: Proceeding of IEEE International Conference on Big Data, IEEE, pp.8765-8767
    資料類型: conference
    DOI 連結: https://doi.org/10.1109/BigData62323.2024.10825707
    DOI: 10.1109/BigData62323.2024.10825707
    顯示於類別:[資訊管理學系] 會議論文

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