The rapid growth of scientific literature has made manual extraction of structured knowledge increasingly impractical. To address this challenge, we introduce SCILIRE, a system for creating datasets from scientific literature. SCILIRE has been designed around Human-AI teaming principles centred on workflows for verifying and curating data. It facilitates an iterative workflow in which researchers can review and correct AI outputs. Furthermore, this interaction is used as a feedback signal to improve future LLM-based inference. We evaluate our design using a combination of intrinsic benchmarking outcomes together with real-world case studies across multiple domains. The results demonstrate that SCILIRE improves extraction fidelity and facilitates efficient dataset creation.

Hassles and uplifts are psychological constructs of individuals’ positive or negative responses to daily minor incidents, with cumulative impacts on mental health. These concepts are largely overlooked in NLP, where existing tasks and models focus on identifying general sentiment expressed in text. These, however, cannot satisfy targeted information needs in psychological inquiry. To address this, we introduce Hassles and Uplifts Detection (HUD), a novel NLP application to identify these constructs in social media language.We evaluate various language models and task adaptation approaches on a probing dataset collected from a private, real-time emotional venting platform. Some of our models achieve F scores close to 80%. We also identify open opportunities to improve affective language understanding in support of studies in psychology.

Information extraction from the scientific literature is a long-standing technique for transforming unstructured knowledge hidden in text into structured data, which can then be used for further analytics and decision-making in downstream tasks. A large body of scientific literature discusses Trust in AI, where factors contributing to human trust in artificial intelligence (AI) applications and technology are studied. It explores questions such as why people may or may not trust a self-driving car, and what factors influence such trust. The relationships of these factors with human trust in AI applications are complex. We explore this space through the lens of information extraction. That is, we investigate how to extract these factors from the literature that studies them. The outcome could inform technology developers to improve the acceptance rate of their products. Our results indicate that (1) while Named Entity Recognition (NER) is largely considered a solved problem in many domains, it is far from solved in extracting factors of human trust in AI from the relevant scientific literature; and, (2) supervised learning is more effective for this task than prompt-based LLMs.

Vent is a specialised iOS/Android social media platform with the stated goal to encourage people to post about their feelings and explicitly label them. In this paper, we study a snapshot of more than 100 million messages obtained from the developers of Vent, together with the labels assigned by the authors of the messages. We establish the quality of the self-annotated data by conducting a qualitative analysis, a vocabulary based analysis, and by training and testing an emotion classifier. We conclude that the self-annotated labels of our corpus are indeed indicative of the emotional contents expressed in the text and thus can support more detailed analyses of emotion expression on social media, such as emotion trajectories and factors influencing them.