discopy 0.1.3

Distributional Compositional Python

Distributional Compositional Python

discopy computes natural language meaning in pictures.

Recipe

1. Draw your picture.

from discopy import Ty, Word, Cup, Wire

s, n = Ty('s'), Ty('n')
Alice, Bob = Word('Alice', n), Word('Bob', n)
loves = Word('loves', n.r @ s @ n.l)

sentence = Alice @ loves @ Bob >> Cup(n, n.r) @ Wire(s) @ Cup(n.l, n)

2. Define a model.

from discopy import Model

ob = {s: 1, n: 2}
ar = {Alice: [1, 0], loves: [0, 1, 1, 0], Bob: [0, 1]}
F = Model(ob, ar)

3. Compute the meaning!

assert F(sentence)

Requirements

• If you just want to play with free categories, there are no requirements.
• If you want to compute matrix-valued functors, you will need numpy.
• If you want to evaluate quantum circuits for real, you will need pytket.
• If you want to differentiate your matrix-valued functors, you will need jax.

Getting Started

Either a) install from pip:

pip install discopy

or b) install from sources:

git clone https://github.com/oxford-quantum-group/discopy.git
cd discopy
python setup.py install

Documentation

For now all of it is in the code. You can use help if needed:

>>> help(discopy.Ty)

Help on class Ty in module discopy.pregroup:

class Ty(discopy.moncat.Ty)
 |  Ty(*t)
 |
 |  Implements pregroup types as lists of simple types.
 |
 |  >>> s, n = Ty('s'), Ty('n')
 |  >>> assert n.l.r == n == n.r.l
 |  >>> assert (s @ n).l == n.l @ s.l and (s @ n).r == n.r @ s.r

You can also checkout the notebooks for a demo!

References

• Lambek (2008) From Word to Sentence: A Computational Algebraic Approach to Grammar
• Clark, Coecke, Sadrzadeh (2008) A Compositional Distributional Model of Meaning
• Grefenstette and Sadrzadeh (2010) Experimental Support for a Categorical Compositional Distributional Model of Meaning
• Coecke (2019) The Mathematics of Text Structure
• De Felice, Meichanetzidis, Toumi (2019) Functorial Question Answering
• Pregroup grammars and categorical compositional distributional semantics and on the nLab