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Found 11 papers, 6 papers with code
PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts
First, the majority of datasets for sequential short-text classification (i. e., classification of short texts that appear in sequences) are small: we hope that releasing a new large dataset will help develop more accurate algorithms for this task.
Transfer Learning for Named-Entity Recognition with Neural Networks
In particular, we demonstrate 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.
NeuroNER: an easy-to-use program for named-entity recognition based on neural networks
Named-entity recognition (NER) aims at identifying entities of interest in a text.
MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks
Over 50 million scholarly articles have been published: they constitute a unique repository of knowledge.
Neural Networks for Joint Sentence Classification in Medical Paper Abstracts
Existing models based on artificial neural networks (ANNs) for sentence classification often do not incorporate the context in which sentences appear, and classify sentences individually.
Feature-Augmented Neural Networks for Patient Note De-identification
In this work, we explore a method to incorporate human-engineered features as well as features derived from EHRs to a neural-network-based de-identification system.
Optimizing Neural Network Hyperparameters with Gaussian Processes for Dialog Act Classification
Therefore it is a useful technique for tuning ANN models to yield the best performances for natural language processing tasks.
De-identification of Patient Notes with Recurrent Neural Networks
It yields an F1-score of 97. 85 on the i2b2 2014 dataset, with a recall 97. 38 and a precision of 97. 32, and an F1-score of 99. 23 on the MIMIC de-identification dataset, with a recall 99. 25 and a precision of 99. 06.
Robust Dialog State Tracking for Large Ontologies
The Dialog State Tracking Challenge 4 (DSTC 4) differentiates itself from the previous three editions as follows: the number of slot-value pairs present in the ontology is much larger, no spoken language understanding output is given, and utterances are labeled at the subdialog level.
Adobe-MIT submission to the DSTC 4 Spoken Language Understanding pilot task
The Dialog State Tracking Challenge 4 (DSTC 4) proposes several pilot tasks.
Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks
Recent approaches based on artificial neural networks (ANNs) have shown promising results for short-text classification.
Ranked #11 on
Dialogue Act Classification
on Switchboard corpus
