DeepAI AI Chat
Log In Sign Up

Dynamic Transfer Learning for Named Entity Recognition

12/13/2018
by   Parminder Bhatia, et al.
Amazon
University of Central Florida
0

State-of-the-art named entity recognition (NER) systems have been improving continuously using neural architectures over the past several years. However, many tasks including NER require large sets of annotated data to achieve such performance. In particular, we focus on NER from clinical notes, which is one of the most fundamental and critical problems for medical text analysis. Our work centers on effectively adapting these neural architectures towards low-resource settings using parameter transfer methods. We complement a standard hierarchical NER model with a general transfer learning framework consisting of parameter sharing between the source and target tasks, and showcase scores significantly above the baseline architecture. These sharing schemes require an exponential search over tied parameter sets to generate an optimal configuration. To mitigate the problem of exhaustively searching for model optimization, we propose the Dynamic Transfer Networks (DTN), a gated architecture which learns the appropriate parameter sharing scheme between source and target datasets. DTN achieves the improvements of the optimized transfer learning framework with just a single training setting, effectively removing the need for exponential search.

READ FULL TEXT

page 1

page 2

page 3

page 4

04/24/2018

Label-aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition

We study the problem of named entity recognition (NER) from electronic m...
05/17/2017

Transfer Learning for Named-Entity Recognition with Neural Networks

Recent approaches based on artificial neural networks (ANNs) have shown ...
11/22/2019

Zero-Resource Cross-Lingual Named Entity Recognition

Recently, neural methods have achieved state-of-the-art (SOTA) results i...
04/05/2019

A Multi-task Learning Approach for Named Entity Recognition using Local Detection

Named entity recognition (NER) systems that perform well require task-re...
09/03/2021

An Open-Source Dataset and A Multi-Task Model for Malay Named Entity Recognition

Named entity recognition (NER) is a fundamental task of natural language...
12/13/2018

End-to-end Joint Entity Extraction and Negation Detection for Clinical Text

Negative medical findings are prevalent in clinical reports, yet discrim...
03/06/2018

CliNER 2.0: Accessible and Accurate Clinical Concept Extraction

Clinical notes often describe important aspects of a patient's stay and ...