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Open AccessProceedings Article

Overview of BioNLP Shared Task 2013

TLDR
The BioNLP Shared Task 2013 shows advances in the state of the art and demonstrates that extraction methods can be successfully generalized in various aspects.
Abstract
The BioNLP Shared Task 2013 is the third edition of the BioNLP Shared Task series that is a community-wide effort to address fine-grained, structural information extraction from biomedical literature. The BioNLP Shared Task 2013 was held from January to April 2013. Six main tasks were proposed. 38 final submissions were received, from 22 teams. The results show advances in the state of the art and demonstrate that extraction methods can be successfully generalized in various aspects.

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Citations
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Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models

TL;DR: This paper proposed a method that takes the advantage of recurrent neural network (RNN) to extract higher level features present across the sentence and achieved state-of-the-art performance on Multi-Level Event Extraction (MLEE) corpus.
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Self-training in significance space of support vectors for imbalanced biomedical event data

TL;DR: A new semi-supervised learning method, based on the training data and the unlabeled data pool, achieves comparable performance to the state-of-the-art systems that are trained on a larger annotated set consisting of training and evaluation data.
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A tree-based neural network model for biomedical event trigger detection

TL;DR: A tree-based neural network model is proposed, which can automatically learn syntactic features from dependency tree for trigger detection and uses a recursive neural network to represent whole dependency tree globally, to better incorporate dependency-based syntax information.
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Using uncertainty to link and rank evidence from biomedical literature for model curation.

TL;DR: A novel method for extracting uncertainty from the literature using a hybrid approach that combines rule induction and machine learning is presented, using subjective logic theory to combine multiple uncertainty values extracted from different sources for the same interaction.
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Linking entities through an ontology using word embeddings and syntactic re-ranking

TL;DR: An unsupervised approach for the linking of named entities to concepts in an ontology/dictionary is proposed by using word embeddings to represent semantic spaces, and a syntactic parser to give higher weight to the most informative word in the named entity mentions.
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