We propose an approach for biomedical information extraction that marries the advantages of machine learning models, e.g., learning directly from data, with the benefits of rule-based approaches, e.g., interpretability. Our approach starts by training a feature-based statistical model, then converts this model to a rule-based variant by converting its features to rules, and "snapping to grid" the feature weights to discrete votes. In doing so, our proposal takes advantage of the large body of work in machine learning, but it produces an interpretable model, which can be directly edited by experts. We evaluate our approach on the BioNLP 2009 event extraction task. Our results show that there is a small performance penalty when converting the statistical model to rules, but the gain in interpretability compensates for that: with minimal effort, human experts improve this model to have similar performance to the statistical model that served as starting point.
Several state-of-the-art event extraction systems employ models based on Support Vector Machines (SVMs) in a pipeline architecture, which fails to exploit the joint dependencies that typically exist among events and arguments. While there have been attempts to overcome this limitation using Markov Logic Networks (MLNs), it remains challenging to perform joint inference in MLNs when the model encodes many high-dimensional sophisticated features such as those essential for event extraction. In this paper, we propose a new model for event extraction that combines the power of MLNs and SVMs, dwarfing their limitations. The key idea is to reliably learn and process high-dimensional features using SVMs; encode the output of SVMs as low-dimensional, soft formulas in MLNs; and use the superior joint inferencing power of MLNs to enforce joint consistency constraints over the soft formulas. We evaluate our approach for the task of extracting biomedical events on the BioNLP 2013, 2011 and 2009 Genia shared task datasets. Our approach yields the best F1 score to date on the BioNLP’13 (53.61) and BioNLP’11 (58.07) datasets and the second-best F1 score to date on the BioNLP’09 dataset (58.16). We describe a system for extracting complex events among genes and proteins from biomedical literature, developed in context of the BioNLP'09 Shared Task on Event Extraction. For each event, its text trigger, class, and arguments are extracted. In contrast to the prevailing approaches in the domain, events can be arguments of other events, resulting in a nested structure that better captures the underlying biological statements. We divide the task into independent steps which we approach as machine learning problems. We define a wide array of features and in particular make extensive use of dependency parse graphs. A rule-based post-processing step is used to refine the output in accordance with the restrictions of the extraction task. In the shared task evaluation, the system achieved an F-score of 51.95 on the primary task, the best performance among the participants. Motivation: In recent years, several biomedical event extraction (EE) systems have been developed. However, the nature of the annotated training corpora, as well as the training process itself, can limit the performance levels of the trained EE systems. In particular, most event-annotated corpora do not deal adequately with coreference. This impacts on the trained systems' ability to recognize biomedical entities, thus affecting their performance in extracting events accurately. Additionally, the fact that most EE systems are trained on a single annotated corpus further restricts their coverage. Results: We have enhanced our existing EE system, EventMine, in two ways. First, we developed a new coreference resolution (CR) system and integrated it with EventMine. The standalone performance of our CR system in resolving anaphoric references to proteins is considerably higher than the best ranked system in the COREF subtask of the BioNLP'11 Shared Task. Secondly, the improved EventMine incorporates domain adaptation (DA) methods, which extend EE coverage by allowing several different annotated corpora to be used during training. Combined with a novel set of methods to increase the generality and efficiency of EventMine, the integration of both CR and DA have resulted in significant improvements in EE, ranging between 0.5 and 3.4 F-Score. The enhanced EventMine outperforms the highest ranked systems from the BioNLP'09 shared task, and from the GENIA and Infectious Diseases subtasks of the BioNLP'11 shared task. Availability: The improved version of EventMine, incorporating the CR system and DA methods, is available at: http: www.nactem.ac.uk EventMine . Contact: makoto.miwa@manchester.ac.uk Biomedical Natural Language Processing (BioNLP) attempts to capture biomedical phenomena from texts by extracting relations between biomedical entities (i.e. proteins and genes). Traditionally, only binary relations have been extracted from large numbers of published papers. Recently, more complex relations (biomolecular events) have also been extracted. Such events may include several entities or other relations. To evaluate the performance of the text mining systems, several shared task challenges have been arranged for the BioNLP community. With a common and consistent task setting, the BioNLP'09 shared task evaluated complex biomolecular events such as binding and regulation.Finding these events automatically is important in order to improve biomedical event extraction systems. In the present paper, we propose an automatic event extraction system, which contains a model for complex events, by solving a classification problem with rich features. The main contributions of the present paper are: (1) the proposal of an effective bio-event detection method using machine learning, (2) provision of a high-performance event extraction system, and (3) the execution of a quantitative error analysis. The proposed complex (binding and regulation) event detector outperforms the best system from the BioNLP'09 shared task challenge. Background We explore techniques for performing model combination between the UMass and Stanford biomedical event extraction systems. Both sub-components address event extraction as a structured prediction problem, and use dual decomposition (UMass) and parsing algorithms (Stanford) to find the best scoring event structure. Our primary focus is on stacking where the predictions from the Stanford system are used as features in the UMass system. For comparison, we look at simpler model combination techniques such as intersection and union which require only the outputs from each system and combine them directly. Motivation: The abundance of biomedical literature has attracted significant interest in novel methods to automatically extract biomedical relations from the literature. Until recently, most research was focused on extracting binary relations such as protein–protein interactions and drug–disease relations. However, these binary relations cannot fully represent the original biomedical data. Therefore, there is a need for methods that can extract fine-grained and complex relations known as biomedical events. Results: In this article we propose a novel method to extract biomedical events from text. Our method consists of two phases. In the first phase, training data are mapped into structured representations. Based on that, templates are used to extract rules automatically. In the second phase, extraction methods are developed to process the obtained rules. When evaluated against the Genia event extraction abstract and full-text test datasets (Task 1), we obtain results with F-scores of 52.34 and 53.34, respectively, which are comparable to the state-of-the-art systems. Furthermore, our system achieves superior performance in terms of computational efficiency. Availability: Our source code is available for academic use at http: dl.dropbox.com u 10256952 BioEvent.zip Contact: [email protected]