CS5012-Speech

Practical 1 for CS5012 Language and Computation module at St Andrews.

Dependencies

The projet also uses the following external Python libraries:

• conllu - accessing language corpora
• nltk - for NLP functionality (probability distributions)
• numpy - for matrix operations
• sklearn - for evaluation functions
• matplotlib- for plotting

How to run

python p1.py

or

python3 p1.py

Command line arguments

The program can be ran with command line arguments to adjust behaviour.

usage: p1.py [-h] [-l LANGS [LANGS ...]] [-u] [-s SMOOTHING] [-w] [-p PLOT] [-c CSV]

optional arguments:
  -h, --help            show this help message and exit
  -l LANGS [LANGS ...], --langs LANGS [LANGS ...]
                        Choose a language from $['EN', 'FR', 'UK', 'PL', 'KO'].
  -u, --unk             Flag indicating if to use the <UNK> tags.
  -s SMOOTHING, --smoothing SMOOTHING
                        Choose smoothing method from ${'WB': <class 'nltk.probability.WittenBellProbDist'>, 'GT': <class
                        'nltk.probability.SimpleGoodTuringProbDist'>}.
  -w, --warnings        Flag to switch on warnings.
  -p PLOT, --plot PLOT  Figure name preffix for a plot. Plot is the working directory unless other existing directory included in filename.
  -c CSV, --csv CSV     Name for csv results file. Saves in the working directory unless other exisitng directory included in filename.

Troubleshooting

In case of errors, please ensure that all all the dependencies are installed and accessible.

Results analysis scrips

To easily analyse the results, I wrote a script to automate running different variations of the model.

To run the experiment script

bash run_experiments.sh <DIR_NAME>

Preferably use <DIR_NAME> that does not exist to prevent data getting overriden.