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LongLexTo

LongLexTo

 
 

screenshots

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LICENSE

LICENSE

 
 

README.md

README.md

 
 

change-file-encoding.ipynb

change-file-encoding.ipynb

 
 

classify-docs.ipynb

classify-docs.ipynb

 
 

conda-dependency-environment.yml

conda-dependency-environment.yml

 
 

predict-new-docs.ipynb

predict-new-docs.ipynb

 
 

predict-plagiarized-docs.ipynb

predict-plagiarized-docs.ipynb

 
 

segment-words-unlabeled.ipynb

segment-words-unlabeled.ipynb

 
 

segment-words.ipynb

segment-words.ipynb

 
 

stop_words_unique.txt

stop_words_unique.txt

 
 

utils.py

utils.py

 
 

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Thai-thesis-classification

Classify each document based on its actual class. Supervised Learning ✨

All .ipynb files can be opened using IPython notebook (currently known as Jupyter notebook)

Follow files in this order:

  1. change-file-encoding
  2. segment-words
  3. classify-docs
  4. segment-words-unlabeled
  5. predict-new-docs
  6. predict-plagiarism-docs

About

Classify each document inside the corpus using Python machine learning module: scikit-learn

docs.google.com/presentation/d/1_58l-VVzTJN0Wr7IIu11eNf-ulLGPdsMG3MKg8DWgGU/edit?usp=sharing

Topics

python nlp segment scikit-learn thai-language thai thai-thesis-classification python2

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Apache-2.0 license
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