Python module to find closest matching words to a given input based on a dictionary of words.
Note : Currently used dictionary was generated from movie reviews data.
- Model can be generated to correct using any list of words (english words, names, places, products, you name it)
- Fast, simple and clean
- Uses fuzzy matching method
- Takes into consideration the relative frequency of usage of words
- Number of suggestions can be varied
- Install the dependencies (given below)
- Run word_corrector.py
- Input a word when prompted. The program will return and display a list of top matches.
- Currently, the dictionary of words has been built using NLTK's movie review data.
- To use another list of words, make a JSON file in
process/source/folder in the following format:
{
"word1" : 1,
"word2" : 2
}i.e. list of key-value pair where word will be key and its frequency/importance will be the value. Order does not matter.
See the movie_review_data.json file for reference.
3. Open process/make_model.py, specify the name of above made source file in main function and run the file.
4. The word_corrector program will now use the new dataset.
- Both python 2 and python 3 are supported.
- numpy : http://docs.scipy.org/doc/numpy-1.10.1/user/install.html
- sklearn : Not required to run the program. Needed only if you want to generate new dataset model.
http://scikit-learn.org/stable/install.html