discopy-nlp

name	discopy-nlp
description	DisCoPy categorical quantum NLP for string diagrams, monoidal categories, and compositional semantics in Python. Use when implementing compositional distributional semantics, building string diagram computations, working with monoidal category theory in code, creating quantum-inspired NLP models, or applying categorical semantics to natural language processing.

DisCoPy Categorical Quantum NLP

DisCoPy provides Python implementations of string diagrams and monoidal categories for compositional NLP.

Installation

pip install discopy

Core Categorical Structures

Types and Morphisms

from discopy import Ty, Box, Id, Diagram

# Types (objects in monoidal category)
n = Ty('n')  # noun
s = Ty('s')  # sentence

# Morphisms (boxes between types)
john = Box('John', Ty(), n)       # John: I → n
sleeps = Box('sleeps', n, s)      # sleeps: n → s

Composition and Tensor

# Sequential composition (;)
john_sleeps = john >> sleeps  # John sleeps: I → s

# Parallel composition (⊗)
mary = Box('Mary', Ty(), n)
john_and_mary = john @ mary  # John ⊗ Mary: I → n ⊗ n

String Diagrams

# Create and draw diagram
diagram = john >> sleeps
diagram.draw()

# Export formats
diagram.draw(path="diagram.png")

Pregroup Grammar

from discopy.grammar.pregroup import Ty, Word, Cup, Id

# Pregroup types with adjoints
n = Ty('n')
s = Ty('s')
n_r = n.r  # right adjoint
n_l = n.l  # left adjoint

# Words with grammatical types
john = Word('John', n)
sleeps = Word('sleeps', n.r @ s)
loves = Word('loves', n.r @ s @ n.l)

# Parse "John sleeps"
sentence = john @ sleeps >> Cup(n, n.r) @ Id(s)

Functorial Semantics

import numpy as np
from discopy.tensor import Tensor, Dim

# Meaning functor: Grammar → Vect
d = Dim(2)

john_vec = Tensor(dom=Dim(1), cod=d, array=np.array([1, 0]))
sleeps_mat = Tensor(dom=d, cod=Dim(1), array=np.array([0.8, 0.2]))

# Compose to get sentence meaning
result = john_vec >> sleeps_mat

Quantum Circuit Semantics

from discopy.quantum import Circuit, Ket, Bra, H, CX

# Map words to quantum circuits
john_circuit = Ket(0)
mary_circuit = Ket(1)
entangle = CX >> (H @ Circuit.id(1))

Categorical Guarantees

DisCoPy ensures:

1. Monoidal Laws: Associativity and unit laws for ⊗
2. Functoriality: Semantic maps preserve composition
3. String Diagram Correctness: Diagrams represent valid morphisms
4. Type Safety: Composition only when types match