Historical-Psychological Text Analysis in Classical Chinese

Core Concepts

The author develops a pipeline, Contextualized Construct Representations (CCR), for historical-psychological text analysis in classical Chinese, combining psychometrics with transformer-based language models.

Abstract

In this work, the authors introduce the Contextualized Construct Representations (CCR) pipeline for historical-psychological text analysis in classical Chinese. The pipeline combines expert knowledge in psychometrics with transformer-based language models to measure psychological constructs from historical corpora. By fine-tuning pre-trained models on a newly created Chinese Historical Psychology Corpus (C-HI-PSY), the CCR method outperforms other approaches and demonstrates superior performance across various tasks. This innovative approach bridges the gap between psychology and natural language processing, offering new insights into historical texts' psychological aspects.

Stats

Humans have produced texts in various languages for thousands of years.
The emerging field of historical psychology relies on computational techniques to extract aspects of psychology from historical corpora.
The CCR method combines expert knowledge in psychometrics with transformer-based language models.
The C-HI-PSY corpus is comprised of 21,539 paragraphs extracted from 667 distinct historical articles and book chapters in classical Chinese.
Officials' attitudes toward reforms are correlated with traditionalism and authority measures derived through CCR.

Quotes

"Contextualized Construct Representations (CCR) outperforms word embedding-based approaches across all tasks."
"The CCR method bridges the gap between psychology and natural language processing."

Key Insights Distilled From

Surveying the Dead Minds

by Yuqi Chen,Si... at arxiv.org 03-04-2024

https://arxiv.org/pdf/2403.00509.pdf

Deeper Inquiries

How can the use of indirect supervised learning impact model performance?

Indirect supervised learning, as used in the context described, involves using similarities between titles as pseudo ground truth for similarities between paragraphs. This approach may introduce noise into the training process because not all content within a paragraph may align perfectly with its title. As a result, some paragraph pairs identified as hard samples by the model might actually contain relevant information despite not aligning closely with their titles. This noise can negatively affect model performance by introducing inaccuracies and inconsistencies in the training data.

What are the implications of benchmarking against historically verified data for validating computational pipelines?

Benchmarking against historically verified data provides a robust validation method for computational pipelines like CCR (Contextualized Construct Representations). By comparing results from these pipelines to known historical outcomes or attitudes, researchers can assess the accuracy and effectiveness of their models in extracting meaningful information from text data. In this case, validating CCR against historical attitudes toward reform and traditionalism allows for an objective assessment of how well it captures psychological constructs in classical Chinese texts. The correlation found between officials' attitudes and measured levels of traditionalism and authority strengthens confidence in CCR's validity.

How might future datasets with expert annotations address limitations in training data quality?

Future datasets with expert annotations can help address limitations related to training data quality by providing more accurate labels or ground truth for models to learn from. Expert annotations ensure that only relevant information is included in the dataset, reducing noise and improving overall data quality. Additionally, expert annotations can offer insights into specific nuances or subtleties within the text that automated methods may overlook. By incorporating such high-quality annotated datasets into model training processes, researchers can enhance model performance, increase interpretability, and mitigate issues stemming from noisy or incomplete training data sets.