The examples folder contains the pre-lab examples we demonstrated in class.

To setup openfst in your machine run.

bash install_openfst.sh

Leave the OPENFST_VERSION=1.6.1, since next versions are not supported for this lab / contain breaking changes.

Install python dependencies with:

pip install -r requirements.txt

Fetch the NLTK Gutenberg corpus using the following script.

python scripts/fetch_gutenberg.py > data/gutenberg.txt

This script downloads and preprocesses the corpus.

We propose the following structure to organize your code.

├── data                            # -> Train and test corpora
│   ├── spell_test.txt              # -> spell checker evaluation corpus
│   └── wiki.txt                    # -> Wikipedial word misspellings
├── fsts                            # -> Compiled FSTs and FST description files
├── install_openfst.sh              # -> OpenFST installation script
├── README.md                       # -> This file.
├── requirements.txt                # -> Python dependencies
├── scripts                         # -> Python and Bash scripts go here
│   ├── fetch_gutenberg.py          # -> Provided script to download the gutenberg corpus
│   ├── helpers.py                  # -> Provided helper functions
│   ├── mkfstinput.py               # -> Provided script to pass a word as input to the spell checker
│   ├── predict.sh                  # -> Provided script to run prediction for a word
│   ├── run_evaluation.py           # -> Provided script to run evaluation on the test corpus
│   ├── util.py                     # -> Stubs to fill in some of your utility functions. TODO
│   ├── w2v_sentiment_analysis.py   # -> Complete the code here for sentiment analysis using word2vec
│   ├── w2v_train.py                # -> Complete the code here to train a word2vec model on the gutenberg corpus
│   └── word_edits.sh               # -> Provided script to get the minimum edit distance edits between two words
└── vocab                           # -> Place your vocab and syms files here

We also propose to use the .fst suffix for fst description files and the .binfst suffix for compiled fsts.

We recommend you study the code we provide before you start and try to run some basic examples. This will give you some basic understanding about how to script for OpenFST. Also, you will probably avoid reimplementing existing functionality

In this part you are going to implement a spell checker using Finite state transducers and the OpenFST library. The spell checker consists of two parts: a Levenshtein transducer and a word acceptor.

Follow the instructions in the lab handouts (available through mycourses) to complete this part.

Once you have implemented a spell checker, e.g. fsts/MY_SPELL_CHECKER.binfst you can use the following script for evaluation on the provided test set.

Run:

python scripts/run_evaluation.py fsts/MY_SPELL_CHECKER.binfst

The script will run the spell checker on the test set and print the model accuracy (percentage of misspelled words that are corrected appropriately).

In this part you will use the gutenberg corpus to train a word2vec model. You will find most similar words, perform word analogies and visualize the embeddings using https://projector.tensorflow.org/. Finally you will use the embeddings you created to perform sentiment analysis on the IMDB movie reviews dataset.

Follow the instructions in the lab handouts (available through mycourses) to complete this part.