This is a comparative study tackling named entity recognition and relation extraction from PubMed abstracts with focus on the gut-brain interplay. The proposed systems for named entity recognition cover a range of models and techniques from traditional gazetteer-based approaches, transformer-based approaches, transformer domain adaptation, large models pre-training as well as LLM prompting. The best performing model among these achieves 82.53% F1-score. The relation extraction task is addressed with ATLOP and LLMs and their best results reach F1 up to 63.80% on binary relation extraction, 89.40% on ternary tag-based relation extraction and 40.32% on ternary mention-based relation extraction.

The last several years have seen a massive increase in the quantity and influence of disinformation being spread online. Various approaches have been developed to target the process at different stages from identifying sources to tracking distribution in social media to providing follow up debunks to people who have encountered the disinformation. One common conclusion in each of these approaches is that disinformation is too nuanced and subjective a topic for fully automated solutions to work but the quantity of data to process and cross-reference is too high for humans to handle unassisted. Ultimately, the problem calls for a hybrid approach of human experts with technological assistance. In this paper we will demonstrate the application of certain state-of-the-art NLP techniques in assisting expert debunkers and fact checkers as well as the role of these NLP algorithms within a more holistic approach to analyzing and countering the spread of disinformation. We will present a multilingual corpus of disinformation and debunks which contains text, concept tags, images and videos as well as various methods for searching and leveraging the content.