markov-chain-sentences

Generates random text based on markov chains using bi-gram pairs created from the the input text.

Usage

Run dotnet run

Example output

First we used a year gestation year. Due to revise the largest with males scraps. Widespread wildcat species began living together later people realized these wildcats thousands of S. Lasted reproduction females weighing as a two tasks generating sentences but they output.

Kitchen benchmark test set of labeled samples in china about generation. Dishes their ability to mcmc sampling to balance teeth. Example using their reputation for sentiment classification is cat. Grow rapidly reaching adult females reach sexual maturity males. More likely in many invalid refinements to function with adult females weighing as four kittens young cat. Generated from ancient egyptians to singapore with another wildcat species began to function with another wildcat felis silvestris healthy. Deep learning based text infilling experimental results generators.

References

• Sources for input.txt:
  • "Deep Learning Based vs. Markov Chain Based Text Generation for Cross Domain Adaptation for Sentiment Classification" link
  • "Show Me How To Revise: Improving Lexically Constrained Sentence Generation with XLNet" from link
  • "Domestic cat" from link
• Wikipedia
• Inspired to do this after encountering "Neural Meduza" and reading an article about this bot