Improving chemical disease relation extraction with rich features and weakly labeled data

By Yifan Peng, Chih-Hsuan Wei, Zhiyong Lu. 2016

81 citations recorded by the source corpus.

BackgroundDue to the importance of identifying relations between chemicals and diseases for new drug discovery and improving chemical safety, there has been a growing interest in developing automatic relation extraction systems for capturing these relations from the rich and rapid-growing biomedical literature. In this work we aim to build on current advances in named entity recognition and a recent BioCreative effort to further improve the state of the art in biomedical relation extraction, in particular for the chemical-induced disease (CID) relations.ResultsWe propose a rich-feature approach with Support Vector Machine to aid in the extraction of CIDs from PubMed articles. Our feature vector includes novel statistical features, linguistic knowledge, and domain resources. We also incorporate the output of a rule-based system as features, thus combining the advantages of rule- and machine learning-based systems. Furthermore, we augment our approach with automatically generated labeled text from an existing knowledge base to improve performance without additional cost for corpus construction. To evaluate our system, we perform experiments on the human-annotated BioCreative V benchmarking dataset and compare with previous results. When trained using only BioCreative V training and development sets, our system achieves an F-score of 57.51 %, which already compares favorably to previous methods. Our system performance was further improved to 61.01 % in F-score when augmented with additional automatically generated weakly labeled data.ConclusionsOur text-mining approach demonstrates state-of-the-art performance in disease-chemical relation extraction. More importantly, this work exemplifies the use of (freely available) curated document-level annotations in existing biomedical databases, which are largely overlooked in text-mining system development.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computational Theory and Mathematics

In Molecular Biology

Citations in the corpus

Chemical-induced disease relation extraction via convolutional neural network (2017, methodology): “In (22), it reported that the KB features and the extra training data can contribute nearly 8.33 and 4.7% to their F -score, respectively.”
Deep learning for extracting protein-protein interactions from biomedical literature (2017, background): “…of biomedical information available in the textual form, there has been considerable interest in applying natural language processing (NLP) techniques and machine learning (ML) methods to the biomedical literature (Huang and Lu, 2015; Leaman and Lu, 2016; Singhal et al., 2016; Peng et al., 2016).”
Marginal Likelihood Training of BiLSTM-CRF for Biomedical Named Entity Recognition from Disjoint Label Sets (2018, methodology): “Weakly Labeled data The addition of weakly labeled data has been used recently to improve the performance of relation extraction systems (Peng et al., 2016; Verga et al., 2018).”