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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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Proceedings ArticleDOI

LitWay, Discriminative Extraction for Different Bio-Events.

TL;DR: It is found that it is difficult for one method to achieve good performance for all semantic relation types due to the complication of bio-events in the literatures, so LitWay, a system for extracting semantic relations from texts, is proposed.
Journal ArticleDOI

BelSmile: a biomedical semantic role labeling approach for extracting biological expression language from text.

TL;DR: The BelSmile system, which uses a semantic-role-labeling (SRL)-based approach to extract the NEs and events for BEL statements, and a syntactic-based labeler to extract subject–verb–object tuples is introduced.
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Kernelized Hashcode Representations for Relation Extraction

TL;DR: This work proposes to use random subspaces of KLSH codes for efficiently constructing an explicit representation of NLP structures suitable for general classification methods using kernelized locality-sensitive hashing (KLSH), and evaluates the proposed approach on biomedical relation extraction datasets.
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Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models

TL;DR: This paper proposes a method that takes the advantage of recurrent neural network (RNN) to extract higher level features present across the sentence and achieves state-of-art F1-score on Multi Level Event Extraction (MLEE) corpus.
Proceedings ArticleDOI

A Search-based Neural Model for Biomedical Nested and Overlapping Event Detection

TL;DR: A novel search-based neural network (SBNN) structured prediction model that treats the nested and overlapping event detection task as a search problem on a relation graph of trigger-argument structures and achieves performance comparable to the state-of-the-art model Turku Event Extraction System without the use of any syntactic and hand-engineered features.
References
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