Investigating Cross-Domain Binary Relation Classification in Biomedical Natural Language Processing.

This paper addresses the challenge of binary relation classification in biomedical Natural Language Processing (NLP), focusing on diverse domains including gene-disease associations, compound protein interactions, and social determinants of health (SDOH). We evaluate different approaches, including fine-tuning Bidirectional Encoder Representations from Transformers (BERT) models and generative Large Language Models (LLMs), and examine their performance in zero and few-shot settings. We also introduce a novel dataset of biomedical text annotated with social and clinical entities to facilitate research into relation classification.

Automated Feature Selection from Medical Literature.

We propose an automated approach to rank the most salient variables related to a certain clinical phenomenon from scientific literature. Our solution is an automated approach to improve the efficiency of the collection of different health-related measures from a population, and to accelerate the discovery of novel associations and dependencies between health-related concepts.

Automatic Mapping of Terminology Items with Transformers.

Biomedical ontologies are a key component in many systems for the analysis of textual clinical data. They are employed to organize information about a certain domain relying on a hierarchy of different classes. Each class maps a concept to items in a terminology developed by domain experts.

Identifying single-item faked responses in personality tests: A new TF-IDF-based method.

Faking in a psychological test is often observed whenever an examinee may gain an advantage from it. Although techniques are available to identify a faker, they cannot identify the specific questions distorted by faking. This work evaluates the effectiveness of term frequency-inverse document frequency (TF-IDF)-an information retrieval mathematical tool used in search engines and language representations-in identifying single-item faked responses.