3,603 research outputs found
New Resources and Perspectives for Biomedical Event Extraction
Event extraction is a major focus of recent work in biomedical information extraction. Despite substantial advances, many challenges still remain for reliable automatic extraction of events from text. We introduce a new biomedical event extraction resource consisting of analyses automatically created by systems participating in the recent BioNLP Shared Task (ST) 2011. In providing for the first time the outputs of a broad set of state-ofthe-art event extraction systems, this resource opens many new opportunities for studying aspects of event extraction, from the identification of common errors to the study of effective approaches to combining the strengths of systems. We demonstrate these opportunities through a multi-system analysis on three BioNLP ST 2011 main tasks, focusing on events that none of the systems can successfully extract. We further argue for new perspectives to the performance evaluation of domain event extraction systems, considering a document-level, “off-the-page ” representation and evaluation to complement the mentionlevel evaluations pursued in most recent work.
Time series kernel similarities for predicting Paroxysmal Atrial Fibrillation from ECGs
We tackle the problem of classifying Electrocardiography (ECG) signals with
the aim of predicting the onset of Paroxysmal Atrial Fibrillation (PAF). Atrial
fibrillation is the most common type of arrhythmia, but in many cases PAF
episodes are asymptomatic. Therefore, in order to help diagnosing PAF, it is
important to design procedures for detecting and, more importantly, predicting
PAF episodes. We propose a method for predicting PAF events whose first step
consists of a feature extraction procedure that represents each ECG as a
multi-variate time series. Successively, we design a classification framework
based on kernel similarities for multi-variate time series, capable of handling
missing data. We consider different approaches to perform classification in the
original space of the multi-variate time series and in an embedding space,
defined by the kernel similarity measure. We achieve a classification accuracy
comparable with state of the art methods, with the additional advantage of
detecting the PAF onset up to 15 minutes in advance
Joint Entity Extraction and Assertion Detection for Clinical Text
Negative medical findings are prevalent in clinical reports, yet
discriminating them from positive findings remains a challenging task for
information extraction. Most of the existing systems treat this task as a
pipeline of two separate tasks, i.e., named entity recognition (NER) and
rule-based negation detection. We consider this as a multi-task problem and
present a novel end-to-end neural model to jointly extract entities and
negations. We extend a standard hierarchical encoder-decoder NER model and
first adopt a shared encoder followed by separate decoders for the two tasks.
This architecture performs considerably better than the previous rule-based and
machine learning-based systems. To overcome the problem of increased parameter
size especially for low-resource settings, we propose the Conditional Softmax
Shared Decoder architecture which achieves state-of-art results for NER and
negation detection on the 2010 i2b2/VA challenge dataset and a proprietary
de-identified clinical dataset.Comment: Accepted at the 57th Annual Meeting of the Association for
Computational Linguistics (ACL 2019
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
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