class CoreNLP:
def __init__(self):
self.parser = CoreNLPDependencyParser(url=self.corenlp_server())
self.sentence_tokenizer = PunktSentenceTokenizer()
@staticmethod
def corenlp_server():
return getenv('CORENLP_SERVER')
def dep_parse(self, text: str, conll_version=10) -> str:
"""Get a CoreNLP depparse,lemma"""
def get_conll(t):
deps, = self.parser.raw_parse(t)
return deps.to_conll(conll_version) # xrenner requires conll10
sentences = self.sentence_tokenizer.sentences_from_text(text)
return '\n'.join(map(get_conll, sentences))
def sentence_split(input_text):
input_text = "<root>" + input_text + "</root>"
soup = BeautifulSoup(input_text, "xml")
paragraphs = []
for doc in soup.find('root').findAll('DOC'):
if doc['type'] == 'story':
headlines = doc('HEADLINE')
for h in headlines:
paragraphs.append(h.contents[0])
p_blocks = doc.find('TEXT').findAll('P')
for p in p_blocks:
paragraphs.append(p.contents[0])
elif doc['type'] == 'multi':
paragraphs.append(doc.find('TEXT').contents[0])
sentences = []
punkt = PunktSentenceTokenizer()
for parag in paragraphs:
for sent in punkt.sentences_from_text(parag, realign_boundaries=True):
sentences.append(replace.sub(' ', sent).strip())
return sentences
def sent_tokenize(text):
model_path = join(dirname(__file__), 'sent_tokenize_model_v1.0.pkl')
with open(model_path, 'rb') as fs:
punkt_param = pickle.load(fs)
punkt_param.sent_starters = {}
abbrev_types = [
'g.m.t', 'e.g', 'dr', 'dr', 'vs', "000", 'mr', 'mrs', 'prof', 'inc',
'tp', 'ts', 'ths', 'th', 'vs', 'tp', 'k.l', 'a.w.a.k.e', 't', 'a.i',
'</i', 'g.w', 'ass', 'u.n.c.l.e', 't.e.s.t', 'ths', 'd.c', 've…', 'ts',
'f.t', 'b.b', 'z.e', 's.g', 'm.p', 'g.u.y', 'l.c', 'g.i', 'j.f', 'r.r',
'v.i', 'm.h', 'a.s', 'bs', 'c.k', 'aug', 't.d.q', 'b…', 'ph', 'j.k',
'e.l', 'o.t', 's.a'
]
abbrev_types.extend(string.ascii_uppercase)
for abbrev_type in abbrev_types:
punkt_param.abbrev_types.add(abbrev_type)
for abbrev_type in string.ascii_lowercase:
punkt_param.abbrev_types.add(abbrev_type)
tokenizer = PunktSentenceTokenizer(punkt_param)
sentences = tokenizer.sentences_from_text(text)
return sentences
