This project is aimed at generating name-like words using various modeling techniques. The project is organized into different subdirectories, each representing a specific modeling approach for word generation. Here's a summary of the project structure and its subdirectories:
- bigram: This subdirectory contains the implementation of a bigram model for generating name-like words. The bigram model is built using character bigrams.
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single layer neural network: This subdirectory contains the implementation of a single layered neural network approach to bigram model for generating name-like words.
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multi layer perceptron: This subdirectory contains the implementation of a multi layer perceptron neural network approach to for generating name-like words. References A Neural Probabilistic Language Model (Yoshua Bengio, et. al. 2003)
The project as a whole aims to explore different approaches to generate words. It encompasses a diverse range of modeling techniques, from simple bigram models to advanced neural network-based models like MLP, RNN, and GRU. The choice of modeling technique may influence the generated words' quality, diversity, and coherence.
To get started with a specific modeling approach, you can navigate to the corresponding subdirectory by following the provided links. Each subdirectory has its own README, which includes detailed information, usage instructions, and any specific requirements for that modeling approach.
Feel free to explore and experiment with the different models to generate name-like words that suit your needs.