mlsanityData to 7 Oct 2026

A Computational Framework to Identify Self-Aspects in Text

arXiv 2507.13115 ↗First posted 17 Jul 2025cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 115 references that are in the map. First posted on arXiv 17 Jul 2025.

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References · 25

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  12. 2019Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailmentcited by 663
13 more
  1. 2018Universal Sentence Encodercited by 485
  2. 2019A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAIcited by 403
  3. 2018On the Robustness of Interpretability Methodscited by 310
  4. 2018Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systemscited by 234
  5. 2019Principles alone cannot guarantee ethical AIcited by 134
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  13. 2019Lemotif: An Affective Visual Journal Using Deep Neural Networkscited by 2

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