# Documents Are People and Words Are Items: A Psychometric Approach to Textual Data with Contextual Embeddings

arXiv: https://arxiv.org/abs/2509.08920 · HTML: https://mlsanity.com/paper/2509.08920/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 10 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings](https://mlsanity.com/paper/1909.00512/) (2019)
- [Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence](https://mlsanity.com/paper/1903.09588/) (2019)

## Built on by (most cited first, 2 of 2)

- [From Text to Parameters: Predicting Item Parameters from Embedding Regularization with Reliability and Design Ceilings](https://mlsanity.com/paper/2607.07141/) (2026)
- [Toward Embedding-Based Psychometrics: Structural Modeling of Assessment-Item Semantics With Contextual Scores](https://mlsanity.com/paper/2609.31976/) (2026)

## Abstract (arXiv)

This research introduces a novel psychometric method for analyzing textual data using large language models. By leveraging contextual embeddings to create contextual scores, we transform textual data into response data suitable for psychometric analysis. Treating documents as individuals and words as items, this approach provides a natural psychometric interpretation under the assumption that certain keywords, whose contextual meanings vary significantly across documents, can effectively differentiate documents within a corpus. The modeling process comprises two stages: obtaining contextual scores and performing psychometric analysis. In the first stage, we utilize natural language processing techniques and encoder based transformer models to identify common keywords and generate contextual scores. In the second stage, we employ various types of factor analysis, including exploratory and bifactor models, to extract and define latent factors, determine factor correlations, and identify the most significant words associated with each factor. Applied to the Wiki STEM corpus, our experimental results demonstrate the method's potential to uncover latent knowledge dimensions and patterns within textual data. This approach not only enhances the psychometric analysis of textual data but also holds promise for applications in fields rich in textual information, such as education, psychology, and law.