most citedTransfer Learning for Named-Entity Recognition with Neural Networks

83 citations · 132 across the 6 of their papers we have counts for

collaborators

11 papers

cs.CL2017★ 35 cited

PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts

Franck Dernoncourt, Ji Young Lee

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 t…

cs.CL2017★ 83 cited

Transfer Learning for Named-Entity Recognition with Neural Networks

Ji Young Lee, Franck Dernoncourt, Peter Szolovits

Recent approaches based on artificial neural networks (ANNs) have shown promising results for named-entity recognition (NER). In order to achieve high performances, ANNs need to be…

cs.CL2017★ 6 cited

NeuroNER: an easy-to-use program for named-entity recognition based on neural networks

Franck Dernoncourt, Ji Young Lee, Peter Szolovits

Named-entity recognition (NER) aims at identifying entities of interest in a text. Artificial neural networks (ANNs) have recently been shown to outperform existing NER systems. Ho…

cs.CL2017

MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks

Ji Young Lee, Franck Dernoncourt, Peter Szolovits

Over 50 million scholarly articles have been published: they constitute a unique repository of knowledge. In particular, one may infer from them relations between scientific concep…

cs.CL2016★ 2 cited

Neural Networks for Joint Sentence Classification in Medical Paper Abstracts

Franck Dernoncourt, Ji Young Lee, Peter Szolovits

Existing models based on artificial neural networks (ANNs) for sentence classification often do not incorporate the context in which sentences appear, and classify sentences indivi…

cs.CL2016★ 3 cited

Feature-Augmented Neural Networks for Patient Note De-identification

Ji Young Lee, Franck Dernoncourt, Ozlem Uzuner +1

Patient notes contain a wealth of information of potentially great interest to medical investigators. However, to protect patients' privacy, Protected Health Information (PHI) must…

cs.CL2016

Optimizing Neural Network Hyperparameters with Gaussian Processes for Dialog Act Classification

Franck Dernoncourt, Ji Young Lee

Systems based on artificial neural networks (ANNs) have achieved state-of-the-art results in many natural language processing tasks. Although ANNs do not require manually engineere…

cs.CL2016

De-identification of Patient Notes with Recurrent Neural Networks

Franck Dernoncourt, Ji Young Lee, Ozlem Uzuner +1

Objective: Patient notes in electronic health records (EHRs) may contain critical information for medical investigations. However, the vast majority of medical investigators can on…

cs.CL2016

Robust Dialog State Tracking for Large Ontologies

Franck Dernoncourt, Ji Young Lee, Trung H. Bui +1

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 lar…

cs.CL2016★ 3 cited

Adobe-MIT submission to the DSTC 4 Spoken Language Understanding pilot task

Franck Dernoncourt, Ji Young Lee, Trung H. Bui +1

The Dialog State Tracking Challenge 4 (DSTC 4) proposes several pilot tasks. In this paper, we focus on the spoken language understanding pilot task, which consists of tagging a gi…

cs.CL2016

Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks

Ji Young Lee, Franck Dernoncourt

Recent approaches based on artificial neural networks (ANNs) have shown promising results for short-text classification. However, many short texts occur in sequences (e.g., sentenc…