Attention-based multi-level feature fusion for named entity recognition

Keyphrases

• Attention-based 100%
• Named Entity Recognition 100%
• Multi-feature Fusion 100%
• Character-level 66%
• Multi-level Features 66%
• Benchmark Dataset 33%
• Capitalization 33%
• Learning Methods 33%
• Natural Language Processing 33%
• Local Character 33%
• Processing Area 33%
• Representation Learning 33%
• Semantic Information 33%
• Sequence Labeling 33%
• Global Character 33%
• Word Level Features 33%
• Character Embedding 33%
• Syntactic Information 33%
• Partial Characteristics 33%
• Complex Sentence 33%
• Word Embedding 33%
• Multi-level Perspective 33%
• Lexical Phrases 33%
• Word Relatedness 33%
• Word-level 33%
• Named Entity 33%
• BiLSTM-CRF 33%

Computer Science

• Feature Fusion 100%
• Named Entity Recognition 100%
• Experimental Result 33%
• Natural Language Processing 33%
• Representation Learning 33%
• Bidirectional Long Short-Term Memory Network 33%
• Word Embedding 33%
• Syntactic Information 33%
• Recognition Result 33%
• Complex Sentence 33%