nltk.metrics.BigramAssocMeasures

class nltk.metrics.BigramAssocMeasures

Bases: 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

classmethod phi_sq(*marginals)

Scores bigrams using phi-square, the square of the Pearson correlation coefficient.

classmethod chi_sq(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.

classmethod fisher(*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.

static dice(n_ii, n_ix_xi_tuple, n_xx)

Scores bigrams using Dice’s coefficient.

classmethod jaccard(*marginals)

Scores ngrams using the Jaccard index.

classmethod likelihood_ratio(*marginals)

Scores ngrams using likelihood ratios as in Manning and Schutze 5.3.4.

static 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.

classmethod pmi(*marginals)

Scores ngrams by pointwise mutual information, as in Manning and Schutze 5.4.

classmethod poisson_stirling(*marginals)

Scores ngrams using the Poisson-Stirling measure.

static raw_freq(*marginals)

Scores ngrams by their frequency

classmethod student_t(*marginals)

Scores ngrams using Student’s t test with independence hypothesis for unigrams, as in Manning and Schutze 5.3.1.