A Survey on Deep Learning for Named Entity Recognition (1812.09449v3)

Abstract: Named entity recognition (NER) is the task to identify mentions of rigid designators from text belonging to predefined semantic types such as person, location, organization etc. NER always serves as the foundation for many natural language applications such as question answering, text summarization, and machine translation. Early NER systems got a huge success in achieving good performance with the cost of human engineering in designing domain-specific features and rules. In recent years, deep learning, empowered by continuous real-valued vector representations and semantic composition through nonlinear processing, has been employed in NER systems, yielding stat-of-the-art performance. In this paper, we provide a comprehensive review on existing deep learning techniques for NER. We first introduce NER resources, including tagged NER corpora and off-the-shelf NER tools. Then, we systematically categorize existing works based on a taxonomy along three axes: distributed representations for input, context encoder, and tag decoder. Next, we survey the most representative methods for recent applied techniques of deep learning in new NER problem settings and applications. Finally, we present readers with the challenges faced by NER systems and outline future directions in this area.

Deep Learning Techniques for Named Entity Recognition: A Comprehensive Survey

The paper "A Survey on Deep Learning for Named Entity Recognition" by Jing Li, Aixin Sun, Jianglei Han, and Chenliang Li provides a thorough examination of the advancements in deep learning strategies for Named Entity Recognition (NER). This survey meticulously categorizes the DL-based NER approaches into specific components: distributed representations for input, context encoders, and tag decoders. It also explores applied deep learning techniques for NER, addressing recent trends such as multi-task learning, transfer learning, reinforcement learning, adversarial learning, and the use of neural attention mechanisms. The authors also highlight key challenges and propose potential future directions for research in NER.

Context and Contributions

NER is a critical task in NLP that involves identifying and classifying entities such as persons, locations, and organizations in text. Traditional NER systems relied heavily on hand-crafted features and domain-specific rules. However, the advent of deep learning techniques has revolutionized NER by enabling automated feature extraction and end-to-end learning. The authors present a new taxonomy to classify DL-based NER approaches, addressing their components separately:

1. Distributed Representations for Input: This section explores word-level, character-level, and hybrid representations. Notably, character-level representations using CNNs and RNNs are highlighted for their ability to handle out-of-vocabulary words and capture sub-word information. The use of pre-trained embeddings like Word2Vec, GloVe, and contextual embeddings such as ELMo and BERT are integral to advancing the performance of NER systems.
2. Context Encoder Architectures: Various architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Recursive Neural Networks, and Transformers are discussed. Each architecture’s ability to model sequential data and dependencies is examined, with specific mentions of their advantages in capturing the context necessary for accurate NER.
3. Tag Decoder Architectures: The paper reviews different approaches for decoding tags, including Multi-Layer Perceptrons (MLP), Conditional Random Fields (CRF), Recurrent Neural Networks, and Pointer Networks. CRFs are notably common due to their ability to capture label transition dependencies, which are crucial for sequential labeling tasks.

Applied Techniques and Practical Implementations

The paper also surveys applied deep learning techniques that augment traditional NER methods, including:

• Multi-task Learning: This technique is employed to learn NER jointly with other related tasks like POS tagging and chunking, benefiting from shared representations across tasks.
• Transfer Learning: Transfer learning approaches, particularly in low-resource and cross-domain settings, are discussed. The use of domain adaptation and parameter-sharing models enable NER systems to perform well even with limited labeled data.
• Reinforcement Learning: This technique is used to dynamically update model states based on feedback from the environment, improving the model’s ability to make decisions on entity classification in a sequential manner.
• Adversarial Learning: Generative Adversarial Networks (GANs) and other adversarial approaches are utilized to make NER models robust against noisy inputs and domain variance.
• Neural Attention Mechanisms: Attention mechanisms are highlighted for their ability to focus on the most relevant parts of the input sequence, improving the performance of NER models by ensuring that significant tokens receive appropriate emphasis during processing.

Challenges and Future Directions

The authors also identify several challenges that remain in the deployment and advancement of NER systems. These include the annotation process, especially for resource-scarce domains and languages, handling informal text, and the need for models capable of detecting unseen entities. Future directions discussed include:

• Fine-grained NER and Boundary Detection: More research is encouraged in fine-grained NER, where entities may have multiple types and nested structures. The decoupling of boundary detection from entity classification could lead to more robust solutions.
• Joint NER and Entity Linking: Integrating NER more closely with entity linking tasks could leverage the enriched semantics from linked entities, enhancing overall accuracy.
• Improving Scalability of DL-based NER: Addressing the computational complexity and required resources for training deep learning models remains critical. Approaches like model compression, pruning, and efficient utilization of pre-trained embeddings are discussed.
• Enhanced Toolkits for DL-based NER: The development of accessible toolkits that standardize modules for data processing, representation, encoding, and decoding is proposed to simplify the deployment of NER systems.

Conclusion

This survey provides an invaluable resource for researchers exploring the utilization and development of deep learning techniques for NER. By consolidating recent advancements and categorizing various approaches, the authors offer insightful guidance on building and enhancing NER systems. Addressing existing challenges and exploring future research directions will contribute significantly to the robustness and applicability of NER in diverse domains and applications.