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De-identification of patient notes with recurrent neural networks
TLDR
The first de-identification system based on artificial neural networks (ANNs), which requires no handcrafted features or rules, unlike existing systems, is introduced, which outperforms the state-of-the-art systems. Expand
Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks
TLDR
This work presents a model based on recurrent neural networks and convolutional neural networks that incorporates the preceding short texts that achieves state-of-the-art results on three different datasets for dialog act prediction. Expand
NeuroNER: an easy-to-use program for named-entity recognition based on neural networks
TLDR
NeuroNER is an easy-to-use named-entity recognition tool based on ANNs that can annotate entities using a graphical web-based user interface (BRAT) and be used to train an ANN, which in turn predict entities’ locations and categories in new texts. Expand
PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts
We present PubMed 200k RCT, a new dataset based on PubMed for sequential sentence classification. The dataset consists of approximately 200,000 abstracts of randomized controlled trials, totaling 2.3Expand
Transfer Learning for Named-Entity Recognition with Neural Networks
TLDR
It is demonstrated that transferring an ANN model trained on a large labeled dataset to another dataset with a limited number of labels improves upon the state-of-the-art results on two different datasets for patient note de-identification. Expand
Neural Networks for Joint Sentence Classification in Medical Paper Abstracts
TLDR
This work presents an ANN architecture that combines the effectiveness of typical ANN models to classify sentences in isolation, with the strength of structured prediction, and outperforms the state-of-the-art results on two different datasets for sequential sentence classification in medical abstracts. Expand
MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks
TLDR
This work presents a system based on a convolutional neural network to extract relations between scientific concepts and ranked first in the SemEval-2017 task 10 (ScienceIE) for relation extraction in scientific articles (subtask C). Expand
Optimizing neural network hyperparameters with Gaussian processes for dialog act classification
TLDR
Using a previously published ANN model yielding state-of-the-art results for dialog act classification, it is demonstrated that optimizing hyperparameters using GP further improves the results, and reduces the computational time by a factor of 4 compared to a random search. Expand
Robust Dialog State Tracking for Large Ontologies
TLDR
A novel dialog state tracking method designed to work robustly under these conditions, using elaborate string matching, coreference resolution tailored for dialogs and a few other improvements is described. Expand
Allosteric Modulation of Intact γ-Secretase Structural Dynamics.
TLDR
This analysis, based on a membrane-coupled anisotropic network model, reveals two types of NCT motions, bending and twisting, with respect to PS1, opening, to the knowledge, new avenues for structure-based design of novel allosteric modulators of γ-secretase protease activity. Expand
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