Structural Equation-VAE: Disentangled Latent Representations for Tabular Data

Learning interpretable latent representations from tabular data remains a challenge in deep generative modeling. We introduce SE-VAE (Structural Equation-Variational Autoencoder), a novel architecture that embeds measurement structure directly into the design of a variational autoencoder. Inspired by structural equation modeling, SE-VAE aligns latent subspaces with known indicator groupings and introduces a global nuisance latent to isolate construct-specific confounding variation. This modular architecture enables disentanglement through design rather than through statistical regularizers alone. We evaluate SE-VAE on a suite of simulated tabular datasets and benchmark its performance against a series of leading baselines using standard disentanglement metrics. SE-VAE consistently outperforms alternatives in factor recovery, interpretability, and robustness to nuisance variation. Ablation results reveal that architectural structure, rather than regularization strength, is the key driver of performance. SE-VAE offers a principled framework for white-box generative modeling in scientific and social domains where latent constructs are theory-driven and measurement validity is essential.

Achieving Semantic Consistency Using BERT: Application of Pre-training Semantic Representations Model in Social Sciences Research

Achieving consistent word interpretations across different time spans is crucial in social sciences research and text analysis tasks, as stable semantic representations form the foundation for research and task correctness, enhancing understanding of socio-political and cultural analysis. Traditional models like Word2Vec have provided significant insights into long-term semantic changes but often struggle to capture stable meanings in short-term contexts, which may be attributed to fluctuations in embeddings caused by unbalanced training data. Recent advancements, particularly BERT (Bidirectional Encoder Representations from Transformers), its pre-trained nature and transformer encoder architecture offer contextual embeddings that improve semantic consistency, making it a promising tool for short-term analysis. This study empirically compares the performance of Word2Vec and BERT in maintaining stable word meanings over time in text analysis tasks relevant to social sciences research. Using articles from the People's Daily spanning 20 years (2004-2023), we evaluated the semantic stability of each model across different timeframes. The results indicate that BERT consistently outperforms Word2Vec in maintaining semantic stability, offering greater stability in contextual embeddings. However, the study also acknowledges BERT's limitations in capturing gradual semantic shifts over longer periods due to its inherent stability. The findings suggest that while BERT is advantageous for short-term semantic analysis in social sciences, researchers should consider complementary approaches for long-term studies to fully capture semantic drift. This research underscores the importance of selecting appropriate word embedding models based on the specific temporal context of social science analyses.