Automatic Extraction of Relationships among Motivations, Emotions and Actions from Natural Language Texts
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Abstract (arXiv)
We propose a new graph-based framework to reveal relationships among motivations, emotions and actions explicitly given natural language texts. A directed acyclic graph is designed to describe human's nature. Nurture beliefs are incorporated to connect outside events and the human's nature graph. No annotation resources are required due to the power of large language models. Amazon Fine Foods Reviews dataset is used as corpus and food-related motivations are focused. Totally 92,990 relationship graphs are generated, of which 63% make logical sense. We make further analysis to investigate error types for optimization direction in future research.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 18 references that are in the map. First posted on arXiv 2 Aug 2024.
Builds on
8 of 18 references.
References · 8
- 2024ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Toolscited by 1,096
- 2013From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviewscited by 107
- 2019ASER: A Large-scale Eventuality Knowledge Graphcited by 89
- 2020GLUCOSE: GeneraLized and COntextualized Story Explanationscited by 85
- 2018Event2Mind: Commonsense Inference on Events, Intents, and Reactionscited by 82
- 2020Recognizing Emotion Cause in Conversationscited by 66
- 2022CICERO: A Dataset for Contextualized Commonsense Inference in Dialoguescited by 38
- 2021ASER: Towards Large-scale Commonsense Knowledge Acquisition via Higher-order Selectional Preference over Eventualitiescited by 28
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Citing papers per year: 2025: 2.
2 papers.