Semantic Scholar Open Access 2018 1419 sitasi

A Survey on Deep Learning for Named Entity Recognition

J. Li Aixin Sun Jianglei Han Chenliang Li

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Computer Science

Penulis (4)

J. Li

Aixin Sun

Jianglei Han

Chenliang Li

Format Sitasi

APA MLA BibTeX

Li, J., Sun, A., Han, J., Li, C. (2018). A Survey on Deep Learning for Named Entity Recognition. https://doi.org/10.1109/TKDE.2020.2981314

Akses Cepat

Lihat di Sumber doi.org/10.1109/TKDE.2020.2981314

Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Total Sitasi: 1419×
Sumber Database: Semantic Scholar
DOI: 10.1109/TKDE.2020.2981314
Akses: Open Access ✓