Compling Final

This project takes a file and returns two.

The first returned file will be phonetically identical to the input, however, every word will be misspelled.

The second returned file will return the input file but with the words that have a phonetically identical twin are substituted.

Usage

If it's the first time you're running it, run: ./telephone.py input_file init to make sure the proper files are installed. Any other execution of the script after the initial run can be formatted as ./telephone.py input_file

Output will be input_file.bad for the misspelled file and input_file.kinda_bad for the similar words file.

Structure

Four modules alongside telephone.py make up this project: mod.py,misspeller.py,unmisspeller.py, dependencies.py

Quick overview of what each module does

• dependencies.py is similar to a requirements.txt, however, all of the installation is done automatically.
• mod.py takes a function and maps it to every token. Then it writes an output file that does not write more than 80 characters per line.
• misspell.py takes a word and returns the phonemes combined into one string
• unmisspell.py reverses the CMU Dictionary to take a combined list of phonemes and return a word instead. unmisspell.py contains a Misspell object which converts a word to phonemes before being converted back to a word again. The original word is returned if there are no identical phonemes.

Justin's Evaluation Criteria

"If it runs / does what you say it does"

• Does what it was intended to do. See above.

"If it uses any tools/techniques/algorithms covered in class"

• The project uses regular expressions to clean some of the phoneme data and word processing data.
• Word tokenization is incorporated to break down text.
• NLTK Packages are used.
• If the project were to be extended, it would implement word vectors to use the Misspeller and UnMisspeller to create puns.

"The code organization and clarity"

• Everything is modular to allow for parts to be added or removed.
• Object Oriented Approach keeps everything clean.
• dependency.py ensures the code will run on any machine with python 3

"How much effort you put into it"

The effort spent on this work went to the following tasks:

• Understanding CMU Dict
• Figuring out the word processor
• Structuring the Misspeller, Word_Modifier, and UnMispeller components to fit together.
• Creating the UnMisspeller
• Debugging

CMU Dict

• http://www.nltk.org/_modules/nltk/corpus/reader/cmudict.html
• http://www.speech.cs.cmu.edu/cgi-bin/cmudict?in=bubonic