Deep Active Learning for Named Entity Recognition (1707.05928v3)

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.

Deep Active Learning for Named Entity Recognition: An Expert Overview

Deep learning has achieved remarkable results across various NLP tasks, including named entity recognition (NER). However, these models typically require substantial amounts of labeled data to reach state-of-the-art performance levels. This paper addresses the challenge of high data requirements by integrating active learning with deep learning, significantly reducing the necessity for large training datasets.

Methodology and Model Architecture

The core innovation is the fusion of active learning with a lightweight CNN-CNN-LSTM architecture. This model leverages convolutional neural networks (CNNs) for character and word-level encoding alongside a long short-term memory (LSTM) decoder for tag prediction, offering nearly equivalent performance to state-of-the-art models with much lower computational overhead.

1. Character-Level Encoder: The model uses CNNs to generate word embeddings from character sequences, effectively handling out-of-vocabulary words and providing fast processing times compared to recurrent alternatives.
2. Word-Level Encoder: Combines word embeddings with character-level representations using CNNs, enabling efficient context capture and robust feature extraction.
3. Tag Decoder: Utilizes an LSTM for tag decoding, chosen for its computational efficiency and competitive performance compared to the more traditional conditional random fields (CRFs).

Active Learning Strategy

The paper implements incremental active learning where the model is retrained iteratively with new data batches. The approach employs uncertainty sampling, focusing on examples where the model predictions show the least confidence. It avoids exhaustive retraining by using incremental updates, making the active learning process feasible in practice.

Key Results

On the OntoNotes-5.0 datasets for both English and Chinese, the proposed method achieved 99% of the performance of the best deep models with only 24.9% and 30.1% of the data, respectively. This represents a significant reduction in labeled data requirements.

Implications and Future Work

The integration of active learning with a lightweight architecture holds promise not only for NER but potentially for other tasks in NLP and beyond. By reducing computational demands and data requirements, the approach aligns well with practical scenarios where labeled data is scarce or costly to obtain. The methodology could be expanded to other structured prediction tasks, enhancing the applicability of deep learning in data-limited environments.

Future research could explore further optimization of active learning heuristics, deployment in real-time applications, and expansions to multilingual settings. Additionally, improvements in sampling strategies might yield even greater efficiency gains, setting a foundation for more robust, adaptable NLP systems.

In conclusion, the paper presents a compelling approach to reducing the dependency on large datasets in training deep models for NER, combining engineering insights with theoretical advancements to streamline the application of AI in data-sensitive contexts.