Python stanford.StanfordParser方法代碼示例

# 需要導入模塊: from nltk.parse import stanford [as 別名]
# 或者: from nltk.parse.stanford import StanfordParser [as 別名]
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)) 

# 需要導入模塊: from nltk.parse import stanford [as 別名]
# 或者: from nltk.parse.stanford import StanfordParser [as 別名]
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') 

# 需要導入模塊: from nltk.parse import stanford [as 別名]
# 或者: from nltk.parse.stanford import StanfordParser [as 別名]
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: ") 

# 需要導入模塊: from nltk.parse import stanford [as 別名]
# 或者: from nltk.parse.stanford import StanfordParser [as 別名]
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') 

# 需要導入模塊: from nltk.parse import stanford [as 別名]
# 或者: from nltk.parse.stanford import StanfordParser [as 別名]
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()) 

# 需要導入模塊: from nltk.parse import stanford [as 別名]
# 或者: from nltk.parse.stanford import StanfordParser [as 別名]
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) 

# 需要導入模塊: from nltk.parse import stanford [as 別名]
# 或者: from nltk.parse.stanford import StanfordParser [as 別名]
def __init__(self):
        self.parser = StanfordParser()