# Natural Language Toolkit: Ngram Association Measures
#
# Copyright (C) 2001-2023 NLTK Project
# Author: Joel Nothman <jnothman@student.usyd.edu.au>
# URL: <https://www.nltk.org/>
# For license information, see LICENSE.TXT
"""
Provides scoring functions for a number of association measures through a
generic, abstract implementation in ``NgramAssocMeasures``, and n-specific
``BigramAssocMeasures`` and ``TrigramAssocMeasures``.
"""
import math as _math
from abc import ABCMeta, abstractmethod
from functools import reduce
_log2 = lambda x: _math.log2(x)
_ln = _math.log
_product = lambda s: reduce(lambda x, y: x * y, s)
_SMALL = 1e-20
try:
from scipy.stats import fisher_exact
except ImportError:
def fisher_exact(*_args, **_kwargs):
raise NotImplementedError
### Indices to marginals arguments:
NGRAM = 0
"""Marginals index for the ngram count"""
UNIGRAMS = -2
"""Marginals index for a tuple of each unigram count"""
TOTAL = -1
"""Marginals index for the number of words in the data"""
[docs]class NgramAssocMeasures(metaclass=ABCMeta):
"""
An abstract class defining a collection of generic association measures.
Each public method returns a score, taking the following arguments::
score_fn(count_of_ngram,
(count_of_n-1gram_1, ..., count_of_n-1gram_j),
(count_of_n-2gram_1, ..., count_of_n-2gram_k),
...,
(count_of_1gram_1, ..., count_of_1gram_n),
count_of_total_words)
See ``BigramAssocMeasures`` and ``TrigramAssocMeasures``
Inheriting classes should define a property _n, and a method _contingency
which calculates contingency values from marginals in order for all
association measures defined here to be usable.
"""
_n = 0
@staticmethod
@abstractmethod
def _contingency(*marginals):
"""Calculates values of a contingency table from marginal values."""
raise NotImplementedError(
"The contingency table is not available" "in the general ngram case"
)
@staticmethod
@abstractmethod
def _marginals(*contingency):
"""Calculates values of contingency table marginals from its values."""
raise NotImplementedError(
"The contingency table is not available" "in the general ngram case"
)
@classmethod
def _expected_values(cls, cont):
"""Calculates expected values for a contingency table."""
n_all = sum(cont)
bits = [1 << i for i in range(cls._n)]
# For each contingency table cell
for i in range(len(cont)):
# Yield the expected value
yield (
_product(
sum(cont[x] for x in range(2**cls._n) if (x & j) == (i & j))
for j in bits
)
/ (n_all ** (cls._n - 1))
)
[docs] @staticmethod
def raw_freq(*marginals):
"""Scores ngrams by their frequency"""
return marginals[NGRAM] / marginals[TOTAL]
[docs] @classmethod
def student_t(cls, *marginals):
"""Scores ngrams using Student's t test with independence hypothesis
for unigrams, as in Manning and Schutze 5.3.1.
"""
return (
marginals[NGRAM]
- _product(marginals[UNIGRAMS]) / (marginals[TOTAL] ** (cls._n - 1))
) / (marginals[NGRAM] + _SMALL) ** 0.5
[docs] @classmethod
def chi_sq(cls, *marginals):
"""Scores ngrams using Pearson's chi-square as in Manning and Schutze
5.3.3.
"""
cont = cls._contingency(*marginals)
exps = cls._expected_values(cont)
return sum((obs - exp) ** 2 / (exp + _SMALL) for obs, exp in zip(cont, exps))
[docs] @staticmethod
def mi_like(*marginals, **kwargs):
"""Scores ngrams using a variant of mutual information. The keyword
argument power sets an exponent (default 3) for the numerator. No
logarithm of the result is calculated.
"""
return marginals[NGRAM] ** kwargs.get("power", 3) / _product(
marginals[UNIGRAMS]
)
[docs] @classmethod
def pmi(cls, *marginals):
"""Scores ngrams by pointwise mutual information, as in Manning and
Schutze 5.4.
"""
return _log2(marginals[NGRAM] * marginals[TOTAL] ** (cls._n - 1)) - _log2(
_product(marginals[UNIGRAMS])
)
[docs] @classmethod
def likelihood_ratio(cls, *marginals):
"""Scores ngrams using likelihood ratios as in Manning and Schutze 5.3.4."""
cont = cls._contingency(*marginals)
return 2 * sum(
obs * _ln(obs / (exp + _SMALL) + _SMALL)
for obs, exp in zip(cont, cls._expected_values(cont))
)
[docs] @classmethod
def poisson_stirling(cls, *marginals):
"""Scores ngrams using the Poisson-Stirling measure."""
exp = _product(marginals[UNIGRAMS]) / (marginals[TOTAL] ** (cls._n - 1))
return marginals[NGRAM] * (_log2(marginals[NGRAM] / exp) - 1)
[docs] @classmethod
def jaccard(cls, *marginals):
"""Scores ngrams using the Jaccard index."""
cont = cls._contingency(*marginals)
return cont[0] / sum(cont[:-1])
[docs]class BigramAssocMeasures(NgramAssocMeasures):
"""
A collection of bigram association measures. Each association measure
is provided as a function with three arguments::
bigram_score_fn(n_ii, (n_ix, n_xi), n_xx)
The arguments constitute the marginals of a contingency table, counting
the occurrences of particular events in a corpus. The letter i in the
suffix refers to the appearance of the word in question, while x indicates
the appearance of any word. Thus, for example:
- n_ii counts ``(w1, w2)``, i.e. the bigram being scored
- n_ix counts ``(w1, *)``
- n_xi counts ``(*, w2)``
- n_xx counts ``(*, *)``, i.e. any bigram
This may be shown with respect to a contingency table::
w1 ~w1
------ ------
w2 | n_ii | n_oi | = n_xi
------ ------
~w2 | n_io | n_oo |
------ ------
= n_ix TOTAL = n_xx
"""
_n = 2
@staticmethod
def _contingency(n_ii, n_ix_xi_tuple, n_xx):
"""Calculates values of a bigram contingency table from marginal values."""
(n_ix, n_xi) = n_ix_xi_tuple
n_oi = n_xi - n_ii
n_io = n_ix - n_ii
return (n_ii, n_oi, n_io, n_xx - n_ii - n_oi - n_io)
@staticmethod
def _marginals(n_ii, n_oi, n_io, n_oo):
"""Calculates values of contingency table marginals from its values."""
return (n_ii, (n_oi + n_ii, n_io + n_ii), n_oo + n_oi + n_io + n_ii)
@staticmethod
def _expected_values(cont):
"""Calculates expected values for a contingency table."""
n_xx = sum(cont)
# For each contingency table cell
for i in range(4):
yield (cont[i] + cont[i ^ 1]) * (cont[i] + cont[i ^ 2]) / n_xx
[docs] @classmethod
def phi_sq(cls, *marginals):
"""Scores bigrams using phi-square, the square of the Pearson correlation
coefficient.
"""
n_ii, n_io, n_oi, n_oo = cls._contingency(*marginals)
return (n_ii * n_oo - n_io * n_oi) ** 2 / (
(n_ii + n_io) * (n_ii + n_oi) * (n_io + n_oo) * (n_oi + n_oo)
)
[docs] @classmethod
def chi_sq(cls, n_ii, n_ix_xi_tuple, n_xx):
"""Scores bigrams using chi-square, i.e. phi-sq multiplied by the number
of bigrams, as in Manning and Schutze 5.3.3.
"""
(n_ix, n_xi) = n_ix_xi_tuple
return n_xx * cls.phi_sq(n_ii, (n_ix, n_xi), n_xx)
[docs] @classmethod
def fisher(cls, *marginals):
"""Scores bigrams using Fisher's Exact Test (Pedersen 1996). Less
sensitive to small counts than PMI or Chi Sq, but also more expensive
to compute. Requires scipy.
"""
n_ii, n_io, n_oi, n_oo = cls._contingency(*marginals)
(odds, pvalue) = fisher_exact([[n_ii, n_io], [n_oi, n_oo]], alternative="less")
return pvalue
[docs] @staticmethod
def dice(n_ii, n_ix_xi_tuple, n_xx):
"""Scores bigrams using Dice's coefficient."""
(n_ix, n_xi) = n_ix_xi_tuple
return 2 * n_ii / (n_ix + n_xi)
[docs]class TrigramAssocMeasures(NgramAssocMeasures):
"""
A collection of trigram association measures. Each association measure
is provided as a function with four arguments::
trigram_score_fn(n_iii,
(n_iix, n_ixi, n_xii),
(n_ixx, n_xix, n_xxi),
n_xxx)
The arguments constitute the marginals of a contingency table, counting
the occurrences of particular events in a corpus. The letter i in the
suffix refers to the appearance of the word in question, while x indicates
the appearance of any word. Thus, for example:
- n_iii counts ``(w1, w2, w3)``, i.e. the trigram being scored
- n_ixx counts ``(w1, *, *)``
- n_xxx counts ``(*, *, *)``, i.e. any trigram
"""
_n = 3
@staticmethod
def _contingency(n_iii, n_iix_tuple, n_ixx_tuple, n_xxx):
"""Calculates values of a trigram contingency table (or cube) from
marginal values.
>>> TrigramAssocMeasures._contingency(1, (1, 1, 1), (1, 73, 1), 2000)
(1, 0, 0, 0, 0, 72, 0, 1927)
"""
(n_iix, n_ixi, n_xii) = n_iix_tuple
(n_ixx, n_xix, n_xxi) = n_ixx_tuple
n_oii = n_xii - n_iii
n_ioi = n_ixi - n_iii
n_iio = n_iix - n_iii
n_ooi = n_xxi - n_iii - n_oii - n_ioi
n_oio = n_xix - n_iii - n_oii - n_iio
n_ioo = n_ixx - n_iii - n_ioi - n_iio
n_ooo = n_xxx - n_iii - n_oii - n_ioi - n_iio - n_ooi - n_oio - n_ioo
return (n_iii, n_oii, n_ioi, n_ooi, n_iio, n_oio, n_ioo, n_ooo)
@staticmethod
def _marginals(*contingency):
"""Calculates values of contingency table marginals from its values.
>>> TrigramAssocMeasures._marginals(1, 0, 0, 0, 0, 72, 0, 1927)
(1, (1, 1, 1), (1, 73, 1), 2000)
"""
n_iii, n_oii, n_ioi, n_ooi, n_iio, n_oio, n_ioo, n_ooo = contingency
return (
n_iii,
(n_iii + n_iio, n_iii + n_ioi, n_iii + n_oii),
(
n_iii + n_ioi + n_iio + n_ioo,
n_iii + n_oii + n_iio + n_oio,
n_iii + n_oii + n_ioi + n_ooi,
),
sum(contingency),
)
[docs]class QuadgramAssocMeasures(NgramAssocMeasures):
"""
A collection of quadgram association measures. Each association measure
is provided as a function with five arguments::
trigram_score_fn(n_iiii,
(n_iiix, n_iixi, n_ixii, n_xiii),
(n_iixx, n_ixix, n_ixxi, n_xixi, n_xxii, n_xiix),
(n_ixxx, n_xixx, n_xxix, n_xxxi),
n_all)
The arguments constitute the marginals of a contingency table, counting
the occurrences of particular events in a corpus. The letter i in the
suffix refers to the appearance of the word in question, while x indicates
the appearance of any word. Thus, for example:
- n_iiii counts ``(w1, w2, w3, w4)``, i.e. the quadgram being scored
- n_ixxi counts ``(w1, *, *, w4)``
- n_xxxx counts ``(*, *, *, *)``, i.e. any quadgram
"""
_n = 4
@staticmethod
def _contingency(n_iiii, n_iiix_tuple, n_iixx_tuple, n_ixxx_tuple, n_xxxx):
"""Calculates values of a quadgram contingency table from
marginal values.
"""
(n_iiix, n_iixi, n_ixii, n_xiii) = n_iiix_tuple
(n_iixx, n_ixix, n_ixxi, n_xixi, n_xxii, n_xiix) = n_iixx_tuple
(n_ixxx, n_xixx, n_xxix, n_xxxi) = n_ixxx_tuple
n_oiii = n_xiii - n_iiii
n_ioii = n_ixii - n_iiii
n_iioi = n_iixi - n_iiii
n_ooii = n_xxii - n_iiii - n_oiii - n_ioii
n_oioi = n_xixi - n_iiii - n_oiii - n_iioi
n_iooi = n_ixxi - n_iiii - n_ioii - n_iioi
n_oooi = n_xxxi - n_iiii - n_oiii - n_ioii - n_iioi - n_ooii - n_iooi - n_oioi
n_iiio = n_iiix - n_iiii
n_oiio = n_xiix - n_iiii - n_oiii - n_iiio
n_ioio = n_ixix - n_iiii - n_ioii - n_iiio
n_ooio = n_xxix - n_iiii - n_oiii - n_ioii - n_iiio - n_ooii - n_ioio - n_oiio
n_iioo = n_iixx - n_iiii - n_iioi - n_iiio
n_oioo = n_xixx - n_iiii - n_oiii - n_iioi - n_iiio - n_oioi - n_oiio - n_iioo
n_iooo = n_ixxx - n_iiii - n_ioii - n_iioi - n_iiio - n_iooi - n_iioo - n_ioio
n_oooo = (
n_xxxx
- n_iiii
- n_oiii
- n_ioii
- n_iioi
- n_ooii
- n_oioi
- n_iooi
- n_oooi
- n_iiio
- n_oiio
- n_ioio
- n_ooio
- n_iioo
- n_oioo
- n_iooo
)
return (
n_iiii,
n_oiii,
n_ioii,
n_ooii,
n_iioi,
n_oioi,
n_iooi,
n_oooi,
n_iiio,
n_oiio,
n_ioio,
n_ooio,
n_iioo,
n_oioo,
n_iooo,
n_oooo,
)
@staticmethod
def _marginals(*contingency):
"""Calculates values of contingency table marginals from its values.
QuadgramAssocMeasures._marginals(1, 0, 2, 46, 552, 825, 2577, 34967, 1, 0, 2, 48, 7250, 9031, 28585, 356653)
(1, (2, 553, 3, 1), (7804, 6, 3132, 1378, 49, 2), (38970, 17660, 100, 38970), 440540)
"""
(
n_iiii,
n_oiii,
n_ioii,
n_ooii,
n_iioi,
n_oioi,
n_iooi,
n_oooi,
n_iiio,
n_oiio,
n_ioio,
n_ooio,
n_iioo,
n_oioo,
n_iooo,
n_oooo,
) = contingency
n_iiix = n_iiii + n_iiio
n_iixi = n_iiii + n_iioi
n_ixii = n_iiii + n_ioii
n_xiii = n_iiii + n_oiii
n_iixx = n_iiii + n_iioi + n_iiio + n_iioo
n_ixix = n_iiii + n_ioii + n_iiio + n_ioio
n_ixxi = n_iiii + n_ioii + n_iioi + n_iooi
n_xixi = n_iiii + n_oiii + n_iioi + n_oioi
n_xxii = n_iiii + n_oiii + n_ioii + n_ooii
n_xiix = n_iiii + n_oiii + n_iiio + n_oiio
n_ixxx = n_iiii + n_ioii + n_iioi + n_iiio + n_iooi + n_iioo + n_ioio + n_iooo
n_xixx = n_iiii + n_oiii + n_iioi + n_iiio + n_oioi + n_oiio + n_iioo + n_oioo
n_xxix = n_iiii + n_oiii + n_ioii + n_iiio + n_ooii + n_ioio + n_oiio + n_ooio
n_xxxi = n_iiii + n_oiii + n_ioii + n_iioi + n_ooii + n_iooi + n_oioi + n_oooi
n_all = sum(contingency)
return (
n_iiii,
(n_iiix, n_iixi, n_ixii, n_xiii),
(n_iixx, n_ixix, n_ixxi, n_xixi, n_xxii, n_xiix),
(n_ixxx, n_xixx, n_xxix, n_xxxi),
n_all,
)
[docs]class ContingencyMeasures:
"""Wraps NgramAssocMeasures classes such that the arguments of association
measures are contingency table values rather than marginals.
"""
[docs] def __init__(self, measures):
"""Constructs a ContingencyMeasures given a NgramAssocMeasures class"""
self.__class__.__name__ = "Contingency" + measures.__class__.__name__
for k in dir(measures):
if k.startswith("__"):
continue
v = getattr(measures, k)
if not k.startswith("_"):
v = self._make_contingency_fn(measures, v)
setattr(self, k, v)
@staticmethod
def _make_contingency_fn(measures, old_fn):
"""From an association measure function, produces a new function which
accepts contingency table values as its arguments.
"""
def res(*contingency):
return old_fn(*measures._marginals(*contingency))
res.__doc__ = old_fn.__doc__
res.__name__ = old_fn.__name__
return res