Slot Filling for Biomedical Information Extraction
Yannis Papanikolaou, Marlene Staib, Justin Grace, Francine Bennett · 2022
Information Extraction (IE) from text refers to the task of extracting structured knowledge from unstructured text.The task typically consists of a series of sub-tasks such as Named Entity Recognition and Relation Extraction.Sourcing entity and relation type specific training data is a major bottleneck in domains with limited resources such as biomedicine.In this work we present a slot filling approach to the task of biomedical IE, effectively replacing the need for entity and relation-specific training data, allowing us to deal with zero-shot settings.We follow the recently proposed paradigm of coupling a Tranformer-based bi-encoder, Dense Passage Retrieval, with a Transformer-based reading comprehension model to extract relations from biomedical text.We assemble a biomedical slot filling dataset for both retrieval and reading comprehension and conduct a series of experiments demonstrating that our approach outperforms a number of simpler baselines.We also evaluate our approach end-to-end for standard as well as zero-shot settings.Our work provides a fresh perspective on how to solve biomedical IE tasks, in the absence of relevant training data.Our code, models and datasets are available at https://github.com/ypapanik/ biomedical-slot-filling.