Open AccessProceedings Article
Overview of BioNLP Shared Task 2013
Claire Nédellec,Robert Bossy,Jin-Dong Kim,Jung-Jae Kim,Tomoko Ohta,Sampo Pyysalo,Pierre Zweigenbaum +6 more
- pp 1-7
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.Citations
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Journal ArticleDOI
Event Trigger Identification for Biomedical Events Extraction Using Domain Knowledge
Deyu Zhou,Dayou Zhong,Yulan He +2 more
TL;DR: Experimental results on the golden standard corpus show that >2.5% improvement on F-score is achieved by the proposed framework when compared with the state-of-the-art approach, demonstrating the effectiveness of the proposed frameworks.
Proceedings ArticleDOI
Relieving the Computational Bottleneck: Joint Inference for Event Extraction with High-Dimensional Features
TL;DR: A new model for event extraction is proposed 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 ofMLNs to enforce joint consistency constraints over the soft formulas.
Journal ArticleDOI
Large-scale automated machine reading discovers new cancer-driving mechanisms.
Marco Antonio Valenzuela-Escárcega,Özgün Babur,Gus Hahn-Powell,Dane Bell,Thomas Hicks,Enrique Noriega-Atala,Xia Wang,Mihai Surdeanu,Emek Demir,Clayton T. Morrison +9 more
TL;DR: Reaching, a system for automated, large-scale machine reading of biomedical papers that can extract mechanistic descriptions of biological processes with relatively high precision at high throughput, demonstrates that combining the extracted pathway fragments with existing biological data analysis algorithms helps identify and explain a large number of previously unidentified mutually exclusive altered signaling pathways in seven different cancer types.
Proceedings ArticleDOI
Biomedical Event Extraction based on Knowledge-driven Tree-LSTM
TL;DR: A novel knowledge base (KB)-driven tree-structured long short-term memory networks (Tree-LSTM) framework is proposed, incorporating two new types of features: dependency structures to capture wide contexts and entity properties from external ontologies via entity linking.
Proceedings ArticleDOI
Training word embeddings for deep learning in biomedical text mining tasks
TL;DR: A biomedical domain-specific word embedding model is presented by incorporating stem, chunk and entity to train word embeddings for biomedical text mining tasks and experimental results show that this model outperform other general-purpose word embedDings.
References
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Proceedings ArticleDOI
Coarse-to-Fine n-Best Parsing and MaxEnt Discriminative Reranking
Eugene Charniak,Mark Johnson +1 more
TL;DR: This paper describes a simple yet novel method for constructing sets of 50- best parses based on a coarse-to-fine generative parser that generates 50-best lists that are of substantially higher quality than previously obtainable.
Proceedings ArticleDOI
Overview of BioNLP'09 Shared Task on Event Extraction
TL;DR: The design and implementation of the BioNLP'09 Shared Task is presented, indicating that state-of-the-art performance is approaching a practically applicable level and revealing some remaining challenges.
Performance measures for information extraction
TL;DR: An error measure is defined, the slot error rate, which combines the different types of error directly, without having to resort to precision and recall as preliminary measures.
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PANTHER Pathway: An Ontology-Based Pathway Database Coupled with Data Analysis Tools
Huaiyu Mi,Paul Thomas +1 more
TL;DR: This chapter first discusses how biological knowledge is represented, particularly the importance of ontologies or standards in systems biology research, and uses PANTHER Pathway as an example to illustrate how ontologies and standards play a role in data modeling, data entry, and data display.
Journal ArticleDOI
Evaluating temporal relations in clinical text: 2012 i2b2 Challenge.
TL;DR: A corpus of discharge summaries annotated with temporal information was provided to be used for the development and evaluation of temporal reasoning systems, and the best systems overwhelmingly adopted a rule based approach for value normalization.