Utilizing character and word embeddings for text normalization with sequence-to-sequence models

Keyphrases

• Annotated Dataset 33%
• Character Embedding 100%
• Character-based 33%
• F1 Score 33%
• Languages Other than English 33%
• Linguistic Information 33%
• Neural Model 33%
• Neural Network 33%
• Neural Network Method 33%
• NLP Tasks 33%
• Parallel Corpus 33%
• Sequence-to-sequence Model 100%
• Shared Task 33%
• Standard Arabic Language 33%
• Subword Information 33%
• Text Normalization 100%
• Word Embedding 100%
• Word Level Features 33%

Computer Science

• Neural Network 50%
• Neural Network Approach 50%
• Parallel Corpus 50%
• Sequence Model 100%
• Word Embedding 100%