Abstract
BackgroundBiomedical event extraction is one of the key tasks in biomedical text mining, supporting various applications such as database curation and hypothesis generation. Several systems, some of which have been applied at a large scale, have been introduced to solve this task.Past studies have shown that the identification of the phrases describing biological processes, also known as trigger detection, is a crucial part of event extraction, and notable overall performance gains can be obtained by solely focusing on this sub-task. In this paper we propose a novel approach for filtering falsely identified triggers from large-scale event databases, thus improving the quality of knowledge extraction.MethodsOur method relies on state-of-the-art word embeddings, event statistics gathered from the whole biomedical literature, and both supervised and unsupervised machine learning techniques. We focus on EVEX, an event database covering the whole PubMed and PubMed Central Open Access literature containing more than 40 million extracted events. The top most frequent EVEX trigger words are hierarchically clustered, and the resulting cluster tree is pruned to identify words that can never act as triggers regardless of their context. For rarely occurring trigger words we introduce a supervised approach trained on the combination of trigger word classification produced by the unsupervised clustering method and manual annotation.ResultsThe method is evaluated on the official test set of BioNLP Shared Task on Event Extraction. The evaluation shows that the method can be used to improve the performance of the state-of-the-art event extraction systems. This successful effort also translates into removing 1,338,075 of potentially incorrect events from EVEX, thus greatly improving the quality of the data. The method is not solely bound to the EVEX resource and can be thus used to improve the quality of any event extraction system or database.AvailabilityThe data and source code for this work are available at: http://bionlp-www.utu.fi/trigger-clustering/.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-016-0070-4) contains supplementary material, which is available to authorized users.
Highlights
Biomedical event extraction is one of the key tasks in biomedical text mining, supporting various applications such as database curation and hypothesis generation
In the first five steps of our method, we focus on the top most frequent trigger words which account for 97.1 % of all events in EVEX
In Section Evaluation of event filtering, we evaluate the impact of trigger pruning on event extraction systems
Summary
Biomedical event extraction is one of the key tasks in biomedical text mining, supporting various applications such as database curation and hypothesis generation. Past studies have shown that the identification of the phrases describing biological processes, known as trigger detection, is a crucial part of event extraction, and notable overall performance gains can be obtained by solely focusing on this sub-task. As a response to the need for advanced literature mining techniques for the biomedical domain, the BioNLP (Biomedical Natural Language Processing) community of researches has emerged. The primary focus of the majority of research within the BioNLP community is to improve information retrieval (IR) and information extraction (IE) in the domain. Event extraction constitutes the identification of biological processes and interactions described in biomedical literature, and their representation as a set of recursive event structures. In subsequent Shared Tasks, while the overall setting remained unchanged, the task has been broadened to cover a large number of additional biological domains and event types [5, 6]
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