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Deep Active Learning for Named Entity Recognition

Shen, Yanyao and Yun, Hyokun and Lipton, Zachary C. and Kronrod, Yakov and Anandkumar, Animashree (2018) Deep Active Learning for Named Entity Recognition. In: 6th International Conference on Learning Representations (ICLR 2018), 30 April-3 May 2018, Vancouver, Canada. http://resolver.caltech.edu/CaltechAUTHORS:20190327-085725408

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Abstract

Deep learning has yielded state-of-the-art performance on many natural language processing tasks including named entity recognition (NER). However, this typically requires large amounts of labeled data. In this work, we demonstrate that the amount of labeled training data can be drastically reduced when deep learning is combined with active learning. While active learning is sample-efficient, it can be computationally expensive since it requires iterative retraining. To speed this up, we introduce a lightweight architecture for NER, viz., the CNN-CNN-LSTM model consisting of convolutional character and word encoders and a long short term memory (LSTM) tag decoder. The model achieves nearly state-of-the-art performance on standard datasets for the task while being computationally much more efficient than best performing models. We carry out incremental active learning, during the training process, and are able to nearly match state-of-the-art performance with just 25\% of the original training data.


Item Type:Conference or Workshop Item (Poster)
Related URLs:
URLURL TypeDescription
https://openreview.net/pdf?id=ry018WZAZPublisherArticle
http://arxiv.org/abs/1707.05928arXivArticle
Subject Keywords:active learning, deep learning, named entity recognition
Record Number:CaltechAUTHORS:20190327-085725408
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20190327-085725408
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94167
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:28 Mar 2019 22:07
Last Modified:28 Mar 2019 22:07

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