Biomedical Relation Extraction: From Binary to Complex

Abstract

Biomedical relation extraction aims to uncover high-quality relations from life science literature with high accuracy and efficiency. Early biomedical relation extraction tasks focused on capturing binary relations, such as protein-protein interactions, which are crucial for virtually every process in a living cell. Information about these interactions provides the foundations for new therapeutic approaches. In recent years, more interests have been shifted to the extraction of complex relations such as biomolecular events. While complex relations go beyond binary relations and involve more than two arguments, they might also take another relation as an argument. In the paper, we conduct a thorough survey on the research in biomedical relation extraction. We first present a general framework for biomedical relation extraction and then discuss the approaches proposed for binary and complex relation extraction with focus on the latter since it is a much more difficult task compared to binary relation extraction. Finally, we discuss challenges that we are facing with complex relation extraction and outline possible solutions and future directions.

Inter-Sentence Relation Extraction for Associating Biological Context with Events in Biomedical Texts

“…Progress has been made in automating biological event extraction from biomedical texts [1], but little attention has been given to identifying and associating the biological context in which such events occur. Biological context, however, often plays a critical role in interpreting these events.…”

“…We make the following contributions in this paper: (1) provide an analysis of the context-event inter-sentential relation extraction problem, (2) develop a corpus of contextevent relations for evaluation, and (3) present first results of an inter-sentential context extraction and association model that provides a baseline for future work.…”

Binary Relation Extraction from Biomedical Literature using Dependency Trees and SVMs

“…IntAct [8]). The types of biological relations in text can be a) semantic, b) grammatical, c) negation and coreference [15]. This work focuses on identifying semantic relations among biological entities.…”

“…Text mining approaches vary from simple co-occurrence [12] -where two entities are considered to be related if they occur in the same abstract, to those using sophisticated Natural Language processing (NLP) [13] and machine learning [14] techniques. Excellent reviews of relation extraction methods can be found in [11], [15], [16].…”

“…In general, a relation extraction system using NLP consists of: a) Named Entity Recognition (NER) [28], b) Relation Trigger Word Identification and c) Relation Extraction [31]. Each of these tasks is non-trivial presenting challenges such as polysemy, affecting both NER and identification of trigger words, owing to which several approaches have been proposed for each of them [15]. While relation trigger word identification is typically used in relation extraction it is not a mandatory step and maybe left out in generalized relation extraction systems.…”