PsyNLP

Program SYnthesis for NLP

PsyNLP is a Python library, that intends to handle morphological inflections for any language in the form of an interpretable program. 🎉

Table of Contents

1. Installation Guidelines
2. Usage
3. Repository structure
4. Running the tests
5. Contribution Guidelines
6. License

Installation Guidelines

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1. Clone the repository

$ git clone git@github.com:Demfier/PsyNLP.git

2. Go to the cloned repository

$ cd PsyNLP

3. Install the dependencies

$ pip3 install -r requirements.txt

Alternatively, you can also install the module from pip directly using the command:

pip3 install psynlp

Usage

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With the power of argparse, the main.py acts as the central script to run any of the pipelines, for any language and training data quality.

• Help menu, for more details:

$ python3 main.py -h

usage: main.py [-h] [-p PIPELINE] [-l LANGUAGE] [-q QUALITY] [-v]

Runs one of the pipeline scripts, for a given language and quality.

optional arguments:
  -h, --help            show this help message and exit
  -p PIPELINE, --pipeline PIPELINE
                        Name of the pipeline file (Default: deterministic)
  -l LANGUAGE, --language LANGUAGE
                        Name of the language (Default: english)
  -q QUALITY, --quality QUALITY
                        Size of the training data (Default: low)
  -v, --verbose         Prints verbose output if specified

• Running a pipeline (say, ostia) for a language (say, polish) and training data quality (say, high):

$ python3 main.py -p ostia -l polish -q high

• Get more output debug-like details with verbose flags (max. 3)

# No verbose, just print the exact word-match accuracy
$ python3 main.py

# Verbose 1, print the expected and actual words
$ python3 main.py -v

# Verbose 2, print the paths responsible for computing an inflection
$ python3 main.py -vv

# Verbose 3, print debug details for PAC and OSTIA
$ python3 main.py -vvv

Repository structure

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• Base classes:

  The code for base classes can be found in the psynlp/core directory.

  • fca.py: Contains implementations of PAC and other methods related to Formal Concept Analysis
  • fst.py: Contains generic Transducer methods, like states and arcs
  • oracle.py: Contains the oracles that're used while computing the PAC basis in fca.py
  • ostia.py: Implementation of the well-known OSTIA algorithm, that uses fst.py

• Pipelines:

  The code for the different pipelines can be found in the psynlp/pipelines directory.

  • deterministic.py : Prediction based on Pandas' group_by (deterministic clustering) and OSTIA RegExp matching
  • ostia.py: Prediction based on just the input-output tapes of OSTIA
  • pac_ostia.py: Prediction based on PAC clusters and OSTIA RegExp matching

• Helpers:

  The code for the different helpers can be found in the psynlp/helpers directory.

  • builtins.py: Monkey-patches some required verbose-related builtin functions
  • importers.py: Includes functions that imports training and testing data into different structures
  • misc.py: Miscellaneous functions
  • text.py: Text-related functions such as inflecting, prefix, suffix, edit distance, etc.

• Data:

  The psynlp/data directory contains all the training and testing data. The files are of the form:

  • {language}-train-{quality}
  • {language}-dev

Running the tests

1. Basic run to check the results:

py.test

2. For debugging:

py.test -s --fulltrace

Contribution Guidelines

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Your contributions are always welcome! Please have a look at the contribution guidelines first. 🎉

License

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MIT License 2018 - Gaurav Sahu and Athitya Kumar.