Paper Reading AI Learner

Surveying the Dead Minds: Historical-Psychological Text Analysis with Contextualized Construct Representation for Classical Chinese

2024-03-01 13:14:45
Yuqi Chen, Sixuan Li, Ying Li, Mohammad Atari

Abstract

In this work, we develop a pipeline for historical-psychological text analysis in classical Chinese. Humans have produced texts in various languages for thousands of years; however, most of the computational literature is focused on contemporary languages and corpora. The emerging field of historical psychology relies on computational techniques to extract aspects of psychology from historical corpora using new methods developed in natural language processing (NLP). The present pipeline, called Contextualized Construct Representations (CCR), combines expert knowledge in psychometrics (i.e., psychological surveys) with text representations generated via transformer-based language models to measure psychological constructs such as traditionalism, norm strength, and collectivism in classical Chinese corpora. Considering the scarcity of available data, we propose an indirect supervised contrastive learning approach and build the first Chinese historical psychology corpus (C-HI-PSY) to fine-tune pre-trained models. We evaluate the pipeline to demonstrate its superior performance compared with other approaches. The CCR method outperforms word-embedding-based approaches across all of our tasks and exceeds prompting with GPT-4 in most tasks. Finally, we benchmark the pipeline against objective, external data to further verify its validity.

Abstract (translated)

在这项工作中,我们开发了一个用于分析古典中文历史心理文本的管道。人类在数千年前生产了各种语言的文本;然而,大多数计算 literature 都关注于当代语言和数据集。随着历史心理领域的兴起,计算技术借助于自然语言处理(NLP)中开发的新方法,从历史语料库中提取心理方面的特点。本文提出的管道,称为上下文建模构建表示(CCR),将心理测量专家知识与通过 Transformer-based 语言模型生成的文本表示相结合,以测量古典中文语料库中的传统主义、规范强度和集体主义等心理概念。考虑到可用数据的有限性,我们提出了一个间接监督的对比学习方法,并构建了第一个中文历史心理学语料库(C-HI-PSY),用于微调预训练模型。我们评估了该管道,以展示其在与其他方法相比的优越性能。CCR 方法在所有任务上都优于基于词嵌入的方法,在大多数任务上超过了 GPT-4 的提示。最后,我们将管道与客观、外部数据进行对比,以进一步验证其实用性。

URL

https://arxiv.org/abs/2403.00509

PDF

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


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot