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Protein-protein interaction (PPI) extraction is an important and widely researched task in the biomedical natural language processing (BioNLP) field. Kernel-based machine learning methods have been used widely to extract PPI automatically, and several kernels focusing on different parts of sentence structure have been published for the PPI task. In this(More)
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.(More)
Because of the importance of proteinprotein interaction (PPI) extraction from text, many corpora have been proposed with slightly differing definitions of proteins and PPI. Since no single corpus is large enough to saturate a machine learning system, it is necessary to learn from multiple different corpora. In this paper, we propose a solution to this(More)
We present a novel end-to-end neural model to extract entities and relations between them. Our recurrent neural network based model captures both word sequence and dependency tree substructure information by stacking bidirectional treestructured LSTM-RNNs on bidirectional sequential LSTM-RNNs. This allows our model to jointly represent both entities and(More)
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(More)
The extraction of bio-molecular events from text is an important task for a number of domain applications such as pathway construction. Several syntactic parsers have been used in Biomedical Natural Language Processing (BioNLP) applications, and the BioNLP 2009 Shared Task results suggest that incorporation of syntactic analysis is important to achieving(More)
In this paper, we present a system, UTTime, which we submitted to TempEval-3 for Task C: Annotating temporal relations. The system uses logistic regression classifiers and exploits features extracted from a deep syntactic parser, including paths between event words in phrase structure trees and their path lengths, and paths between event words in(More)
MOTIVATION Event extraction using expressive structured representations has been a significant focus of recent efforts in biomedical information extraction. However, event extraction resources and methods have so far focused almost exclusively on molecular-level entities and processes, limiting their applicability. RESULTS We extend the event extraction(More)
Research into event-based text mining from the biomedical literature has been growing in popularity to facilitate the development of advanced biomedical text mining systems. Such technology permits advanced search, which goes beyond document or sentence-based retrieval. However, existing event-based systems typically ignore additional information within the(More)
Protein-protein interaction extraction is a challenging information extraction task in the BioNLP field. Several kernels focusing on a part of syntactic information have been proposed for the task. In this paper, we propose a method to combine multiple layers of syntactic information by using a combination of multiple kernels based on several different(More)