Discovering Semantic Subdimensions through Disentangled Conceptual Representations (2025)

Abstract: Understanding the core dimensions of conceptual semantics is fundamental to uncovering how meaning is organized in language and the brain. Existing approaches often rely on predefined semantic dimensions that offer only broad representations, overlooking finer conceptual distinctions. This paper proposes a novel framework to investigate the subdimensions underlying coarse-grained semantic dimensions. Specifically, we introduce a \textbf{D} isentangled \textbf{C} ontinuous \textbf{S} emantic \textbf{R} epresentation \textbf{M} odel ( \textbf{DCSRM} ) that decomposes word embeddings from large language models into multiple sub-embeddings, each encoding specific semantic information. Using these subembeddings, we identify a set of interpretable semantic subdimensions. To assess their neural plausibility, we apply voxel-wise encoding models to map these subdimensions to brain activation. Our work offers more fine-grained interpretable semantic subdimensions of conceptual meaning. Further analyses reveal that semantic dimensions are structured according to distinct principles, with polarity emerging as a key factor driving their decomposition into subdimensions. The neural correlates of the identified subdimensions support their cognitive and neuroscientific plausibility.

## introduction core dimensions are fundamental to structure mental representations, enabling systematic classification, context-sensitive interpretation, and generalization across novel situations @xcite @xcite . in perceptual domains like vision, core dimensions include color, shape, motion, depth, and texture @xcite @xcite ; in audition, they include pitch, loudness, timbre, spatial location, and rhythm @xcite @xcite . * corresponding authors. these dimensions support object recognition (e.g., a red, round, shiny, motionless @xmath0.g., a loud sound in a @xmath1. crucially, this dimensional structure enables generalization by allowing novel stimuli to be interpreted based on their positions within a shared representational space. extending this framework to conceptual semantics is more challenging, as the underlying dimensions are more abstract and less perceptually grounded. one promising approach to addressing the above challenges is to empirically define a set of semantic dimensions @xcite @xcite . @xcite introduced 65 semantic dimensions grounded in neuroscience research on conceptual representations. however, recent studies have shown that these 65 dimensions exhibit substantial overlap and lack neurobiological plausibility @xcite @xcite . to address this, @xcite proposed six major semantic dimensions: vision, action, space, time, social, and emotion. follow-up neuroimaging studies have validated the robustness of these six dimensions @xcite @xcite . nevertheless, this set of interpretable dimensions offers a coarse-grained representation of semantic space, which is limited in its ability to capture finer semantic distinctions that are crucial for accurately modeling the conceptual meaning @xcite . this paper proposes a novel framework for investigating the subdimensions underlying six semantic dimensions. to establish neural validity, we examine their corresponding representational patterns in the brain. specifically, we introduce a disentangled continuous semantic representation model (dcsrm), which decomposes word embeddings from large language models into multiple semanticspecific sub-embeddings. each sub-embedding is learned using a multi-objective optimization approach to maximize the encoding of its target semantic while minimizing interference from others. we then interpret the meaning of the semantic subdimensions captured by these sub-embeddings by inspecting words with high and low loadings. to assess their neural plausibility, voxel-wise encoding models @xcite are applied to map these subdimensions onto brain responses during natural language comprehension. our work offers a more fine-grained and interpretable set of semantic subdimensions for representing conceptual meaning than the conventional six semantic dimensions. these subdimensions are shown to be structured according to distinct principles, with polarity emerging as a key factor driving their decomposition. moreover, we identify neural correlates of these subdimensions, aligning with prior neuroimaging studies @xcite , thereby supporting their cognitive and neuroscientific validity. to summarize, our main contributions include: ## methods to investigate the subdimensions of each semantic dimension and their neural representations, we propose a three-step framework (figure a3 ): 1) use dcsrm to encode each semantic information into a low-dimensional sub-embedding; 2) analyze the sub-embedding dimensions to identify semantic subdimensions; and 3) perform voxel-wise encoding to examine their relationship with brain activity during natural story comprehension. ## experimental setup brain imaging data. we use the chinese fmri dataset from the smn4lang @xcite , which was collected from 12 native speakers as they listened to 60 stories from the renmin daily review foot_1 . these stories covered a broad range of topics, with each story lasting between 4 to 7 minutes, resulting in approximately 5 hours of audio content. the text and audio for all stories were downloaded from the renmin daily review website, and the text was manually verified for consistency with the audio. the total word count across all stories was 43,326, forming a vocabulary of 9,153 unique words. after data collection, the fmri data were preprocessed following the human connectome project (hcp) pipeline @xcite . semantic rating data. our study utilized the rating dataset from the ssdd @xcite , which includes subjective ratings for 17,940 chinese words. it focuses on six semantic dimensions: vision, action, social, emotion, space, and time. these 17,940 words encompass nearly all commonly used chinese words, including verbs, nouns, adjectives, adverb, and quantifiers, etc. a 1-7 scale (7 = very high, 1 = very low) for all six semantic dimensions, based on the given definitions. for each word on each dimension, the final rating was obtained by averaging the 30 individual scores. these semantic ratings accurately reflect the extent to which a concept involves information related to each dimension. table a5 shows six-dimension semantic ratings for several concepts. for instance, the word "justice" received a score of 1 on the time dimension and 1.133 on the space dimension, indicating that it lacks temporal information and contains only a minimal amount of spatial information. ## conclusions our primary goal is to investigate the fine-grained structure of conceptual semantics. a key challenge lies in defining and quantifying semantic subdimensions. to address this, we propose a semantic representation disentangling model with an interpretable framework that decomposes word embeddings into multiple sub-embeddings, each capturing distinct semantic content and corresponding to a specific subdimension. these subdimensions are shown to be structured according to distinct principles, with polarity emerging as a key factor driving their decomposition. neural encoding analyses further confirm their representation in the brain. compared to traditional approaches, our method automatically uncovers more fine-grained semantic dimensions from large-scale data without manually specifying their number or type, offering a data-driven foundation for constructing a conceptual semantic representation system that supports systematic classification, context-sensitive interpretation, and generalization to novel situations.