nltk.parse.nonprojectivedependencyparser.NaiveBayesDependencyScorer

class documentation

class NaiveBayesDependencyScorer(DependencyScorerI): (source)

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

Method	`__init__`	Undocumented
Method	`score`	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.
Method	`train`	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.
Instance Variable	`classifier`	Undocumented

def __init__(self): (source) ¶

overrides nltk.parse.nonprojectivedependencyparser.DependencyScorerI.__init__

Undocumented

def score(self, graph): (source) ¶

overrides nltk.parse.nonprojectivedependencyparser.DependencyScorerI.score

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.
Returns
3 dimensional list	Edge scores for the graph parameter.

def train(self, graphs): (source) ¶

overrides nltk.parse.nonprojectivedependencyparser.DependencyScorerI.train

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.

classifier = (source) ¶

Undocumented