CovidBERT-Biomedical Relation Extraction for Covid-19

Kernel-based learning for biomedical relation extraction

Biomedical Relation Extraction: From Binary to Complex

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 therapeut...

Exploiting Rich Syntactic Information for Relation Extraction from Biomedical Articles∗

This paper proposes a ternary relation extraction method primarily based on rich syntactic information. We identify PROTEIN-ORGANISM-LOCATION relations in the text of biomedical articles. Different kernel functions are used with an SVM learner to integrate two sources of information from syntactic parse trees: (i) a large number of syntactic features that have been shown useful for Semantic Rol...

Exploiting graph kernels for high performance biomedical relation extraction

BACKGROUND Relation extraction from biomedical publications is an important task in the area of semantic mining of text. Kernel methods for supervised relation extraction are often preferred over manual feature engineering methods, when classifying highly ordered structures such as trees and graphs obtained from syntactic parsing of a sentence. Tree kernels such as the Subset Tree Kernel and Pa...

Noise Reduction Methods for Distantly Supervised Biomedical Relation Extraction

Distant supervision has been applied to automatically generate labeled data for biomedical relation extraction. Noise exists in both positively and negativelylabeled data and affects the performance of supervised machine learning methods. In this paper, we propose three novel heuristics based on the notion of proximity, trigger word and confidence of patterns to leverage lexical and syntactic i...