21,433 research outputs found
Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text
We advance the state of the art in biomolecular interaction extraction with
three contributions: (i) We show that deep, Abstract Meaning Representations
(AMR) significantly improve the accuracy of a biomolecular interaction
extraction system when compared to a baseline that relies solely on surface-
and syntax-based features; (ii) In contrast with previous approaches that infer
relations on a sentence-by-sentence basis, we expand our framework to enable
consistent predictions over sets of sentences (documents); (iii) We further
modify and expand a graph kernel learning framework to enable concurrent
exploitation of automatically induced AMR (semantic) and dependency structure
(syntactic) representations. Our experiments show that our approach yields
interaction extraction systems that are more robust in environments where there
is a significant mismatch between training and test conditions.Comment: Appearing in Proceedings of the Thirtieth AAAI Conference on
Artificial Intelligence (AAAI-16
Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction Mention Extraction
Social media is an useful platform to share health-related information due to
its vast reach. This makes it a good candidate for public-health monitoring
tasks, specifically for pharmacovigilance. We study the problem of extraction
of Adverse-Drug-Reaction (ADR) mentions from social media, particularly from
twitter. Medical information extraction from social media is challenging,
mainly due to short and highly information nature of text, as compared to more
technical and formal medical reports.
Current methods in ADR mention extraction relies on supervised learning
methods, which suffers from labeled data scarcity problem. The State-of-the-art
method uses deep neural networks, specifically a class of Recurrent Neural
Network (RNN) which are Long-Short-Term-Memory networks (LSTMs)
\cite{hochreiter1997long}. Deep neural networks, due to their large number of
free parameters relies heavily on large annotated corpora for learning the end
task. But in real-world, it is hard to get large labeled data, mainly due to
heavy cost associated with manual annotation. Towards this end, we propose a
novel semi-supervised learning based RNN model, which can leverage unlabeled
data also present in abundance on social media. Through experiments we
demonstrate the effectiveness of our method, achieving state-of-the-art
performance in ADR mention extraction.Comment: Accepted at DTMBIO workshop, CIKM 2017. To appear in BMC
Bioinformatics. Pls cite that versio
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