def nonprojective_conll_parse_demo():
graphs = [DependencyGraph(entry)
for entry in conll_data2.split('\n\n') if entry]
npp = ProbabilisticNonprojectiveParser()
npp.train(graphs, NaiveBayesDependencyScorer())
parse_graph = npp.parse(['Cathy', 'zag', 'hen', 'zwaaien', '.'], ['N', 'V', 'Pron', 'Adj', 'N', 'Punc'])
print(parse_graph)
def nonprojective_conll_parse_demo():
from nltk.parse.dependencygraph import conll_data2
graphs = [DependencyGraph(entry) for entry in conll_data2.split("\n\n") if entry]
npp = ProbabilisticNonprojectiveParser()
npp.train(graphs, NaiveBayesDependencyScorer())
for parse_graph in npp.parse(["Cathy", "zag", "hen", "zwaaien", "."], ["N", "V", "Pron", "Adj", "N", "Punc"]):
print(parse_graph)
def nonprojective_conll_parse_demo():
graphs = [
DependencyGraph(entry) for entry in conll_data2.split('\n\n') if entry
]
npp = ProbabilisticNonprojectiveParser()
npp.train(graphs, NaiveBayesDependencyScorer())
parse_graph = npp.parse(['Cathy', 'zag', 'hen', 'zwaaien', '.'],
['N', 'V', 'Pron', 'Adj', 'N', 'Punc'])
print(parse_graph)
def nonprojective_conll_parse_demo():
from nltk.parse.dependencygraph import conll_data2
graphs = [DependencyGraph(entry) for entry in conll_data2.split("\n\n") if entry]
npp = ProbabilisticNonprojectiveParser()
npp.train(graphs, NaiveBayesDependencyScorer())
for parse_graph in npp.parse(
["Cathy", "zag", "hen", "zwaaien", "."], ["N", "V", "Pron", "Adj", "N", "Punc"]
):
print(parse_graph)
def projective_prob_parse_demo():
"""
A demo showing the training and use of a projective
dependency parser.
"""
graphs = [DependencyGraph(entry) for entry in conll_data2.split("\n\n") if entry]
ppdp = ProbabilisticProjectiveDependencyParser()
print "Training Probabilistic Projective Dependency Parser..."
ppdp.train(graphs)
sent = ["Cathy", "zag", "hen", "wild", "zwaaien", "."]
print "Parsing '", " ".join(sent), "'..."
parse = ppdp.parse(sent)
print "Parse:"
print parse[0]
def projective_prob_parse_demo():
"""
A demo showing the training and use of a projective
dependency parser.
"""
graphs = [DependencyGraph(entry)
for entry in conll_data2.split('\n\n') if entry]
ppdp = ProbabilisticProjectiveDependencyParser()
print('Training Probabilistic Projective Dependency Parser...')
ppdp.train(graphs)
sent = ['Cathy', 'zag', 'hen', 'wild', 'zwaaien', '.']
print('Parsing \'', " ".join(sent), '\'...')
parse = ppdp.parse(sent)
print('Parse:')
print(parse[0])
def projective_prob_parse_demo():
"""
A demo showing the training and use of a projective
dependency parser.
"""
graphs = [DependencyGraph(entry)
for entry in conll_data2.split('\n\n') if entry]
ppdp = ProbabilisticProjectiveDependencyParser()
print('Training Probabilistic Projective Dependency Parser...')
ppdp.train(graphs)
sent = ['Cathy', 'zag', 'hen', 'wild', 'zwaaien', '.']
print('Parsing \'', " ".join(sent), '\'...')
print('Parse:')
for tree in ppdp.parse(sent):
print(tree)
def projective_prob_parse_demo():
"""
A demo showing the training and use of a projective
dependency parser.
"""
from nltk.parse.dependencygraph import conll_data2
graphs = [DependencyGraph(entry) for entry in conll_data2.split("\n\n") if entry]
ppdp = ProbabilisticProjectiveDependencyParser()
print("Training Probabilistic Projective Dependency Parser...")
ppdp.train(graphs)
sent = ["Cathy", "zag", "hen", "wild", "zwaaien", "."]
print("Parsing '", " ".join(sent), "'...")
print("Parse:")
for tree in ppdp.parse(sent):
print(tree)