AI Bibliography
AI Bibliography |
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Yadav, V., & Bethard, S. (2019). A survey on recent advances in named entity recognition from deep learning models. arXiv preprint arXiv:1910.11470. |
| Resource type: Journal Article BibTeX citation key: Yadav2019 View all bibliographic details  |
Categories: Artificial Intelligence, Computer Science, Data Sciences, General, Mathematics, Military Science Subcategories: Cognitive Electronic Warfare, Cross-domain deterrence, Cyber, Deep learning, Machine learning, Markov models, Networked forces, Neural nets, Q-learning Creators: Bethard, Yadav Publisher: Collection: arXiv preprint arXiv:1910.11470 |
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Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. NER systems have been studied and developed widely for decades, but accurate systems using deep neural networks (NN) have only been introduced in the last few years. We present a comprehensive survey of deep neural network architectures for NER, and contrast them with previous approaches to NER based on feature engineering and other supervised or semi-supervised learning algorithms. Our results highlight the improvements achieved by neural networks, and show how incorporating some of the lessons learned from past work on feature-based NER systems can yield further improvements.
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