Email updates

Keep up to date with the latest news and content from BMC Medical Informatics and Decision Making and BioMed Central.

This article is part of the supplement: Proceedings of the ACM Fifth International Workshop on Data and Text Mining in Biomedical Informatics (DTMBio 2011)

Open Access Proceedings

Detecting modification of biomedical events using a deep parsing approach

Andrew MacKinlay12*, David Martinez12* and Timothy Baldwin12

Author Affiliations

1 Department of Computing and Information Systems, University of Melbourne, VIC 3010, Australia

2 NICTA Victoria Research Laboratories, University of Melbourne, VIC 3010, Australia

For all author emails, please log on.

BMC Medical Informatics and Decision Making 2012, 12(Suppl 1):S4  doi:10.1186/1472-6947-12-S1-S4

Published: 30 April 2012

Abstract

Background

This work describes a system for identifying event mentions in bio-molecular research abstracts that are either speculative (e.g.

    analysis
of IkappaBalpha phosphorylation, where it is not specified whether phosphorylation did or did not occur) or negated (e.g.
    inhibition
of IkappaBalpha phosphorylation
, where phosphorylation did not occur). The data comes from a standard dataset created for the BioNLP 2009 Shared Task. The system uses a machine-learning approach, where the features used for classification are a combination of shallow features derived from the words of the sentences and more complex features based on the semantic outputs produced by a deep parser.

Method

To detect event modification, we use a Maximum Entropy learner with features extracted from the data relative to the trigger words of the events. The shallow features are bag-of-words features based on a small sliding context window of 3-4 tokens on either side of the trigger word. The deep parser features are derived from parses produced by the English Resource Grammar and the RASP parser. The outputs of these parsers are converted into the Minimal Recursion Semantics formalism, and from this, we extract features motivated by linguistics and the data itself. All of these features are combined to create training or test data for the machine learning algorithm.

Results

Over the test data, our methods produce approximately a 4% absolute increase in F-score for detection of event modification compared to a baseline based only on the shallow bag-of-words features.

Conclusions

Our results indicate that grammar-based techniques can enhance the accuracy of methods for detecting event modification.