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.

翻译：深层学习在许多自然语言处理任务(包括名称实体识别(NER))中产生了最先进的表现。 但是,这通常需要大量标签数据。 在这项工作中,我们证明,当深层学习与积极学习相结合时,标签培训数据的数量可以大幅降低。虽然积极学习具有抽样效率,但由于需要迭代再培训,它可以计算成本很高。为了加快这一速度,我们为NER引入了一个轻量级结构,即CNN-CNN-LSTM模型,由连动字符和单词编码器以及长期短期内存标记(LSTM)解码器组成。该模型在任务的标准数据集上几乎实现了最先进的性能,同时在计算上比最佳性能模型效率高得多。我们在培训过程中开展了渐进式的积极学习,并且能够与最初培训数据的25 ⁇ 几乎匹配最新性能。