Python nltk.parse.stanford.StanfordParser() Examples
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Example #1
Source Project: acl2017-interactive_summarizer Author: UKPLab File: corpus_cleaner.py License: Apache License 2.0 | 6 votes |
def __init__(self, datasets_path, corpus_name, parse_type, lang='english'): self.datasets_path = datasets_path self.corpus_name = corpus_name self.corpus_path = path.join(datasets_path, corpus_name) self.docs_path = path.join(self.corpus_path, "docs") self.topics_file = path.join(self.corpus_path, "topics.xml") self.models_path = path.join(self.corpus_path, "models") self.smodels_path = path.join(self.corpus_path, "smodels") self.jar_path = path.join(PROJECT_PATH, "summarizer", "jars") os.environ['CLASSPATH'] = self.jar_path self.cleaned_path = path.join(datasets_path, "processed") if parse_type == 'parse': if lang == 'english': self.parser = stanford.StanfordParser(model_path="%s/englishPCFG.ser.gz" % (self.jar_path)) if lang == 'german': self.parser = stanford.StanfordParser(model_path="%s/germanPCFG.ser.gz" % (self.jar_path)) # self.cleaned_path = path.join(datasets_path, "processed.parse") if parse_type == 'props': # TODO if lang == 'english': self.props_parser = ClausIE.get_instance() if lang == 'german': self.parser = stanford.StanfordParser(model_path="%s/germanPCFG.ser.gz" % (self.jar_path))
Example #2
Source Project: py-nltk-svo Author: klintan File: svo.py License: MIT License | 5 votes |
def __init__(self): """ Initialize the SVO Methods """ self.noun_types = ["NN", "NNP", "NNPS", "NNS", "PRP"] self.verb_types = ["VB", "VBD", "VBG", "VBN", "VBP", "VBZ"] self.adjective_types = ["JJ", "JJR", "JJS"] self.pred_verb_phrase_siblings = None self.parser = stanford.StanfordParser() self.sent_detector = nltk.data.load('tokenizers/punkt/english.pickle')
Example #3
Source Project: readAI Author: ayoungprogrammer File: readai.py License: GNU General Public License v2.0 | 5 votes |
def main(argv): debug = False try: opts, args = getopt.getopt(argv, "hd",["help","debug"]) except getopt.GetoptError as e: usage() sys.exit(2) for opt, arg in opts: if opt in ["-h", "help"]: usage() sys.exit(2) if opt in ["-d", "debug"]: debug = True parser = stanford.StanfordParser() line = raw_input("Enter line: ") while line != 'stop': sent = list(parser.raw_parse(line))[0] if debug: print sent # print parse tree if sent[0].label() == "SBARQ": print answer(sent) else: try: describe(sent) except ValueError as e: print "Error describing sentence. " + e if debug: print smap # print semantic map line = raw_input("Enter line: ")
Example #4
Source Project: RDF-Triple-API Author: tdpetrou File: rdf_triple.py License: MIT License | 5 votes |
def clear_data(self): self.parser = stanford.StanfordParser(model_path=r"/users/ted/stanford nlp/stanford-parser-full-2015-01-30/stanford-parser-3.5.1-models/edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz") self.first_NP = '' self.first_VP = '' self.parse_tree = None self.subject = RDF_Triple.RDF_SOP('subject') self.predicate = RDF_Triple.RDF_SOP('predicate', 'VB') self.Object = RDF_Triple.RDF_SOP('object')
Example #5
Source Project: StrepHit Author: Wikidata File: extract_sentences.py License: GNU General Public License v3.0 | 5 votes |
def setup_extractor(self): self.splitter = PunktSentenceSplitter(self.language) self.parser = StanfordParser(path_to_jar='dev/stanford-corenlp-3.6.0.jar', path_to_models_jar='dev/stanford-corenlp-3.6.0-models.jar', java_options=' -mx2G -Djava.ext.dirs=dev/') self.token_to_lemma = {} for lemma, tokens in self.lemma_to_token.iteritems(): for t in tokens: self.token_to_lemma[t] = lemma self.all_verbs = set(self.token_to_lemma.keys())
Example #6
Source Project: StrepHit Author: Wikidata File: compute_lu_distribution.py License: GNU General Public License v3.0 | 5 votes |
def main(corpus, verbs, processes, outfile, sub_sentences): """ Compute the LU distribution in the corpus, i.e. how many LUs per sentence """ global splitter, tagger, parser, all_verbs splitter = PunktSentenceSplitter('en') tagger = TTPosTagger('en') parser = StanfordParser(path_to_jar='dev/stanford-corenlp-3.6.0.jar', path_to_models_jar='dev/stanford-corenlp-3.6.0-models.jar', java_options=' -mx1G -Djava.ext.dirs=dev/') # no way to make classpath work all_verbs = reduce(lambda x, y: x.union(y), imap(set, json.load(verbs).values()), set()) all_verbs.discard('be') all_verbs.discard('have') args = load_corpus(corpus, 'bio', text_only=True) worker = worker_with_sub_sentences if sub_sentences else worker_with_sentences counter = defaultdict(int) for i, counts in enumerate(parallel.map(worker, args, processes)): for k, v in counts.iteritems(): counter[k] += v if (i + 1) % 10000 == 0: logger.info('Processed %d documents', i + 1) counter = OrderedDict(sorted(counter.items(), key=lambda (k, v): k)) for k, v in counter.iteritems(): print k, v json.dump(counter, outfile, indent=2)
Example #7
Source Project: Lango Author: ayoungprogrammer File: parser.py License: GNU General Public License v2.0 | 5 votes |
def __init__(self): self.parser = StanfordParser()
Example #8
Source Project: Lango Author: ayoungprogrammer File: parser.py License: GNU General Public License v2.0 | 5 votes |
def __init__(self): self.parser = StanfordParser( model_path='edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz') stanford_dir = self.parser._classpath[0].rpartition('/')[0] self.parser._classpath = tuple(find_jars_within_path(stanford_dir))