A Computational Framework to Identify Self-Aspects in Text

TL;DR

This paper proposes a computational framework to identify Self-aspects in text, aiming to advance NLP analysis of the multifaceted Self concept across disciplines like psychology and phenomenology.

Contribution

It introduces an ontology and annotated dataset for Self-aspects, and evaluates various models for their effectiveness in identifying these aspects in text.

Findings

Development of an ontology and annotated dataset for Self-aspects

Evaluation of discriminative, generative, and embedding models for accuracy and interpretability

Application of top models in mental health and phenomenology case studies

Abstract

This Ph.D. proposal introduces a plan to develop a computational framework to identify Self-aspects in text. The Self is a multifaceted construct and it is reflected in language. While it is described across disciplines like cognitive science and phenomenology, it remains underexplored in natural language processing (NLP). Many of the aspects of the Self align with psychological and other well-researched phenomena (e.g., those related to mental health), highlighting the need for systematic NLP-based analysis. In line with this, we plan to introduce an ontology of Self-aspects and a gold-standard annotated dataset. Using this foundation, we will develop and evaluate conventional discriminative models, generative large language models, and embedding-based retrieval approaches against four main criteria: interpretability, ground-truth adherence, accuracy, and computational efficiency. Top-performing models will be applied in case studies in mental health and empirical phenomenology.