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)