nltk.parse.NaiveBayesDependencyScorer

class nltk.parse.NaiveBayesDependencyScorer

Bases: DependencyScorerI

A dependency scorer built around a MaxEnt classifier. In this particular class that classifier is a NaiveBayesClassifier. It uses head-word, head-tag, child-word, and child-tag features for classification.

>>> from nltk.parse.dependencygraph import DependencyGraph, conll_data2

>>> graphs = [DependencyGraph(entry) for entry in conll_data2.split('\n\n') if entry]
>>> npp = ProbabilisticNonprojectiveParser()
>>> npp.train(graphs, NaiveBayesDependencyScorer())
>>> parses = npp.parse(['Cathy', 'zag', 'hen', 'zwaaien', '.'], ['N', 'V', 'Pron', 'Adj', 'N', 'Punc'])
>>> len(list(parses))
1

__init__()

train(graphs)

Trains a NaiveBayesClassifier using the edges present in graphs list as positive examples, the edges not present as negative examples. Uses a feature vector of head-word, head-tag, child-word, and child-tag.

Parameters

graphs (list(DependencyGraph)) – A list of dependency graphs to train the scorer.

score(graph)

Converts the graph into a feature-based representation of each edge, and then assigns a score to each based on the confidence of the classifier in assigning it to the positive label. Scores are returned in a multidimensional list.

Parameters

graph (DependencyGraph) – A dependency graph to score.

Return type

3 dimensional list

Returns

Edge scores for the graph parameter.