Character-Aware Neural Language Models
TensorFlow implementation of character-aware neural language models using CNN, highway networks, and LSTM.
What is Character-Aware Neural Language Models?
lstm-char-cnn-tensorflow is a TensorFlow implementation of character-aware neural language models that process text at both character and word levels. It combines convolutional neural networks for character embeddings with highway networks and LSTM layers to create sophisticated language models that can capture morphological patterns and long-range dependencies in text.
Machine learning researchers and NLP practitioners interested in character-level language modeling, particularly those working with TensorFlow who want to experiment with hybrid character-word architectures.
This implementation provides a clean, modular codebase for the influential Character-Aware Neural Language Models paper, offering researchers a starting point for experimenting with character-level representations in language models without building from scratch.
Overview
in progress
Use Cases
Best For
- Research on character-level language modeling architectures
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