def __init__(self, src, src_folder="../data/", corenlp_path="../stanford-corenlp/",\
regexp=None, verbose=False):
# initialization
self.src = src
self.filename = re.search("([A-Za-z|.]+\-*[A-Za-z|.]+\-*[A-Za-z|.]+\_.*).json", self.src).group(1)
self.src_candidates = os.path.join(src_folder, "lexicon/candidates.json")
self.dst_starred = os.path.join(src_folder, "starred_reviews/")
self.corenlp_path = os.path.normpath(corenlp_path)
self.regexp = regexp
self.verbose = verbose
self.entity = {}
self.entity_name = ""
self.entity_regexp = []
self.ratings = []
self.sentiment_words = []
self.adv_adj_combinations = {}
self.clean_reviews = []
self.starred_reviews = []
self.stopwords = set(stopwords.words("english"))
self.stopwords.remove("not")
self.stemmer = SnowballStemmer("english")
# need set the CORENLP_HOME path
os.environ["CORENLP_HOME"] = self.corenlp_path
self.corenlp = corenlp.CoreNLPClient(annotators="tokenize ssplit dcoref".split(), timeout=50000)
def get_openIE_triples(document):
# CoreNLP OpenIE
"""
Input: document: str
Output: tripleSet: set(tripple-tuple, confidence)
"""
tripleSet = set()
try:
with corenlp.CoreNLPClient(
annotators="tokenize,ssplit,pos,lemma,depparse,natlog,openie".
split()) as client:
ann = client.annotate(document) # 'doc.CoreNLP_pb2.Document'>
for sent in ann.sentence:
if len(sent.openieTriple) > 0: # if there are any triples...
# <class 'google.protobuf.pyext._message.RepeatedCompositeContainer'>
triples = sent.openieTriple
for t in triples:
triple_tuple = (t.subject, t.relation, t.object)
c = t.confidence
out = (triple_tuple, c)
tripleSet.add(out)
except:
# corenlp server probably timed out because the document was too big
# TODO: grab a smaller section and annotate that.
pass
return tripleSet
def test_tokensregex():
with corenlp.CoreNLPClient(
annotators='tokenize ssplit ner depparse'.split(),
timeout=60000) as client:
# Example pattern from: https://nlp.stanford.edu/software/tokensregex.shtml
pattern = '([ner: PERSON]+) /wrote/ /an?/ []{0,3} /sentence|article/'
matches = client.tokensregex(TEXT, pattern)
assert len(matches["sentences"]) == 1
assert matches["sentences"][0]["length"] == 1
assert matches == {
"sentences": [
{
"0": {
"text": "Chris wrote a simple sentence",
"begin": 0,
"end": 5,
"1": {
"text": "Chris",
"begin": 0,
"end": 1
}
},
"length": 1
},
]
}
def find_all_pos_words(pos): results = [] verbs = [] with corenlp.CoreNLPClient(annotators="tokenize ssplit pos".split()) as client: for idx in tqdm(range(tokenizer.vocab_size)): word = tokenizer._convert_id_to_token(idx) if check_word_pos(word, pos): ann = client.annotate(word) pos_all = [[token.pos for token in sent.token] for sent in ann.sentence] pos_list = [] for pos_sent in pos_all: pos_list.extend(pos_sent) if len(pos_list) == 1 and pos_list[0] not in \ ['VVD', 'VVG', 'VVN', 'VVP', 'VVZ', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ', 'VHD', 'VHG', 'VHN', 'VHP', 'VHZ', 'MD']: results.append(idx) verbs.append(word) # for idx in tqdm(range(tokenizer.vocab_size)): # word = tokenizer._convert_id_to_token(idx) # if check_word_pos(word, pos): # results.append(idx) # verbs.append(word) # print(results) print(len(results)) return results, verbs
def tokenize_text(text):
"""
Tokenizes the title's text by the embedding class
:param title: The title to tokenize its text
"""
# Filters out external links
text = ' '.join([
word for word in text.split()
if "https://" not in word and "http://" not in word
])
# Start the coreNLPServer separately
with corenlp.CoreNLPClient(
start_server=False,
timeout=10000,
annotators="tokenize ssplit lemma pos".split()) as client:
ann = client.annotate(text)
# Filters out stop words
stop_words = set(stopwords.words('english')) | {
word.capitalize()
for word in stopwords.words('english')
}
# Removes punctuation from each word
punctuation = set(string.punctuation) | {"\"\""} | {'\'\''} | {'``'}
punctuation_and_stop_words = stop_words | punctuation
# Couple each word with its pos tag if the word isn't a stop word and isn't a punctuation
text = [
f"{token.lemma}_{TREEBANK_TO_UNIVERSAL[token.pos]}"
for sentence in ann.sentence for token in sentence.token
if token.lemma not in punctuation_and_stop_words
]
return text
def annotate_question(question, table_id, dir_in, dir_out, split):
if not os.path.isdir(dir_out):
os.makedirs(dir_out)
ftable = os.path.join(dir_in, split) + '.tables.jsonl'
fout = os.path.join(
dir_out,
split) + datetime.datetime.now().strftime("%Y%m%d%H%M%S") + '.jsonl'
fbase = os.path.join(dir_in, split) + '_base.jsonl'
copyfile(fbase, fout)
with open(ftable) as ft, open(fout, 'a') as fo, corenlp.CoreNLPClient(
annotators="tokenize ssplit".split()) as client:
tables = {}
for line in ft:
d = json.loads(line)
tables[d['id']] = d # to get table headers
raw_data = {
"phase": 1,
"table_id": table_id,
"question": question,
"sql": {
"sel": 0,
"conds": [[0, 0, 0]],
"agg": 0
}
}
a = annotate_example(client, raw_data, tables[table_id])
# if not is_valid_example(a):
# raise Exception(str(a))
nlp = spacy.load('en_core_web_sm')
doc = nlp(question)
a["question"]["ent"] = [token.tag_ for token in doc]
fo.write('\n' + json.dumps(a) + '\n')
return fout, tables[table_id]['header']
def main(args):
num_sentences = 0
with open(args.input, encoding='utf-8') as f, open(args.output, mode='w', encoding='utf-8') as out:
with corenlp.CoreNLPClient(annotators="tokenize ner".split(), endpoint="http://localhost:5000") as client:
for line in f.readlines():
mrp_json = json.loads(line)
tok = []
ner = []
ann = client.annotate(mrp_json['input'], output_format='json')
for sentence in ann['sentences']:
for tokens in sentence['tokens']:
tok.append(tokens['word'])
ner.append(tokens['ner'])
if len(ner) != len(mrp_json['nodes']):
print(mrp_json['id'], " error!")
mrp_json['tok'] = tok
mrp_json['ner'] = ner
# for ner, nodes in zip(ner, mrp_json['nodes']):
# nodes['properties'].append('ner')
# nodes['values'].append(ner)
out.write(json.dumps(mrp_json) + '\n')
num_sentences += 1
if num_sentences % 1000 == 0:
print(f"Processed {num_sentences} sentences!")
def test_tokenizer():
cases = [
(u"RT @ #happyfuncoding: this is a typical Twitter tweet :-)",
u"rt @ #happyfuncoding : this is a typical twitter tweet :-)".split()
),
(u"HTML entities & other Web oddities can be an ácute <em class='grumpy'>pain</em> >:(",
u"html entities and other web oddities can be an ácute".split() +
[u"<em class='grumpy'>", u"pain", u"</em>", u">:("]),
(u"It's perhaps noteworthy that phone numbers like +1 (800) 123-4567, (800) 123-4567, and 123-4567 are treated as words despite their whitespace.",
u"it's perhaps noteworthy that phone numbers like".split() + [
u"+1 (800) 123-4567", u",", u"(800) 123-4567", u",", u"and",
u"123-4567"
] + u"are treated as words despite their whitespace .".split())
]
annotator = HappyFunTokenizer()
annotator.start()
try:
with corenlp.CoreNLPClient(
properties=annotator.properties,
annotators="happyfun ssplit pos".split()) as client:
for text, tokens in cases:
ann = client.annotate(text)
tokens_ = [t.word for t in ann.sentence[0].token]
assert tokens == tokens_
finally:
annotator.terminate()
annotator.join()
def corenlp_start():
os.environ['CORENLP_HOME'] = './corenlp'
corenlp_client = corenlp.CoreNLPClient(endpoint="http://localhost:9000", memory='16G',
annotators="tokenize ssplit pos lemma depparse natlog openie".split())
corenlp_client.annotate('This is a dummy input sentence to initialize all CoreNLP components.')
print('(Re)initialized coreNLP server...')
return corenlp_client
def qgnet_main(args_dict):
# first, run shell script, if necessary, in qgnet to create model
subprocess.call([
'../{}/download_QG-Net.sh'.format(settings.qgnet_dir),
args_dict['qgnet_path']
])
# second, pre-process the pdfs
jsonObj, allDocs = load_data('{}/da_embeddings.txt'.format(
settings.models_dir))
abstracts = []
for value in jsonObj.values():
if "summary" in value['metadata']:
abstracts.append(value['metadata']["summary"])
elif "abstract" in value['metadata']:
abstracts.append(value['metadata']["abstract"])
nlp = corenlp.CoreNLPClient(output_format='json',
properties={'timeout': '50000'})
features, tfidf = create_tf_idf(abstracts, False)
for i, abstract in enumerate(abstracts):
preprocess_pdf(abstract, features[i, :].toarray(), tfidf, nlp)
# third, generate qg-net questions
subprocess.call([
'../{}/qg_reproduce_LS.sh'.format(settings.qgnet_dir),
args_dict['qgnet_path'], settings.models_dir
])
def __init__(self, ontology_name):
self.causal_headers = ["Source_File", 'Query', "Score", "Span", "Relation Index", "Relation", "Relation_Type",
"Indicator", "Cause Index", "Cause", "Effect Index", "Effect", "Sentence"]
self.event_headers = ["Source_File", 'Query', "Score", "Event Index", "Span", "Sentence Span","Relation", "Event_Type",
"FrameNet_Frame", "Indicator", "Location", "Time", 'Agent Index', "Agent",
'Patient Index', "Patient", "Sentence"]
self.entity_headers = ["Source_File", 'Query', "Score", "Entity Index", "Span", "Sentence Span", "Entity", "Entity_Type",
"FrameNet_Frame", "Indicator", "Qualifier", "Sentence"]
self.variable_headers = ["Source_File", 'Sentence', 'Indicator', 'Scoring', 'Index']
self.entity_index = 0
self.event_index = 0
self.variable_index = 0
self.causal_index = 0
self.ontology_name = ontology_name
if os.getenv('CORENLP_HOME') is not None and os.getenv('CORENLP_HOME') != '':
print(f'using Stanford CoreNLP Server @ {os.getenv("CORENLP_HOME")}')
self.CoreNLPclient = corenlp.CoreNLPClient( start_server=True,
be_quiet=True,
timeout=100000,
annotators=['tokenize',
'ssplit',
'pos',
'parse',
'lemma',
'ner',
'depparse'])
self.CoreNLPclient.annotate("hello world") # warmup the CoreNLP client and start the java server
else:
raise ValueError('the "CORENLP_HOME" environment variable is not set, cannot run Stanford CoreNLP Server')
def __init__(self, corenlp_path):
self.keyPhrases = {}
self.rels = ['IsA', 'UsedFor', 'PartOf', 'HasA', 'CreatedBy', 'MadeOf']
os.environ['CORENLP_HOME'] = corenlp_path
self.CoreNLPclient = corenlp.CoreNLPClient(annotators=[
'tokenize', 'ssplit', 'pos', 'depparse', 'lemma', 'parse'
])
def __init__(self, corenlp_path, previous_def_file):
self.nounTags=['NN', 'NNP', 'NNS', "NNPS",'VBG']
#os.environ['CORENLP_HOME'] = '/Users/evangeliaspiliopoulou/Desktop/stanfordCoreNLP'
os.environ['CORENLP_HOME'] = corenlp_path
self.CoreNLPclient = corenlp.CoreNLPClient(annotators=['tokenize', 'ssplit', 'pos', 'lemma'])
if previous_def_file!= None:
self.previous_def = json.load(open(previous_def_file))
self.exceptionTerms= set()
def start():
timer = Timer()
timer.start('Start CoreNLP server')
Corenlp.client = corenlp.CoreNLPClient(
annotators=Corenlp.DEFAULT_ANNOTATORS,
properties=Corenlp.DEFAULT_PROPERTIES)
timer.stop()
def Parse(text, annotators=None):
if annotators == None:
# annotators = ['tokenize', 'ssplit', 'lemma', 'pos', 'ner', 'parse', 'depparse', 'regnexer','coref']
annotators = ['tokenize', 'ssplit', 'lemma', 'pos', 'parse']
with corenlp.CoreNLPClient(annotators=annotators,
properties=StanfordCoreNLP_chinese_properties,
timeout=15000) as client:
ann = client.annotate(text)
return ann
def POSTag(text, sent_split=True, tolist=True):
words = []
if text != '':
try:
lang = langdetect.detect(text)
except langdetect.lang_detect_exception.LangDetectException:
lang = "undetermined"
if (lang == "zh-cn"): #If text is chinese segment, else leave it
#########
if sent_split:
annotators = ['tokenize', 'ssplit', 'pos']
with corenlp.CoreNLPClient(
annotators=annotators,
properties=StanfordCoreNLP_chinese_properties,
timeout=15000) as client:
ann = client.annotate(text)
words = [[(token.word, token.pos) for token in sent.token]
for sent in ann.sentence]
segmented_list = [
' '.join(['#'.join(posted) for posted in wordlist])
for wordlist in words
]
segmented = '\n'.join(segmented_list)
else:
annotators = ['tokenize', 'pos']
with corenlp.CoreNLPClient(
annotators=annotators,
properties=StanfordCoreNLP_chinese_properties,
timeout=15000) as client:
ann = client.annotate(text)
words = [(token.word, token.pos)
for token in ann.sentencelessToken]
segmented = ' '.join(['#'.join(posted) for posted in words])
else:
segmented = text
words = segmented.split()
else:
segmented = text
if tolist:
return words #list
else:
return segmented #string
def __init__(self):
#if not os.environ.get('CORENLP_HOME'):
os.environ['CORENLP_HOME'] = os.path.abspath(
os.path.join(os.path.dirname(__file__),
'../../third_party/stanford-corenlp-full-2018-10-05'))
if not os.path.exists(os.environ['CORENLP_HOME']):
raise Exception(
f'''Please install Stanford CoreNLP and put it at {os.environ['CORENLP_HOME']}.
Direct URL: http://nlp.stanford.edu/software/stanford-corenlp-full-2018-10-05.zip
Landing page: https://stanfordnlp.github.io/CoreNLP/''')
self.client = corenlp.CoreNLPClient()
def Chinese_CoreNLP_test(text=None, annotators=None):
if text == None:
text = ("国务院日前发出紧急通知，要求各地切实落实保证市场供应的各项政策，维护副食品价格稳定。")
####
if annotators == None:
annotators = ['tokenize', 'ssplit', 'pos']
# annotators = ['tokenize', 'ssplit', 'lemma', 'pos', 'ner', 'depparse']
with corenlp.CoreNLPClient(annotators=annotators,
properties=StanfordCoreNLP_chinese_properties,
timeout=15000) as client:
ann = client.annotate(text)
# sent_list = [token.word for token in ann.sentence[0].token]
# ['国务院', '日前', '发出', '紧急', '通知', '，', '要求', '各地', '切实', '落实', '保证', '市场', '供应', '的', '各', '项', '政策', '，', '维护', '副食品', '价格', '稳定', '。']
return ann
def __init__(self):
if not os.environ.get("CORENLP_HOME"):
os.environ["CORENLP_HOME"] = os.path.abspath(
os.path.join(
os.path.dirname(__file__),
"/corenlp/stanford-corenlp-full-2018-10-05",
))
if not os.path.exists(os.environ["CORENLP_HOME"]):
raise Exception(
"""Please install Stanford CoreNLP and put it at {}.
Direct URL: http://nlp.stanford.edu/software/stanford-corenlp-full-2018-10-05.zip
Landing page: https://stanfordnlp.github.io/CoreNLP/""".format(
os.environ["CORENLP_HOME"]))
self.client = corenlp.CoreNLPClient()
def get_examples_from_forums(forums, guest_client):
""" Convert reviews from a list of forums into classification examples.
Args:
forums: A list of forum ids
conference: Conference name (from openreview_lib.Conference)
Returns:
A list of openreview_lib.ClassificationExamples
"""
sid_map, pairs = orl.get_review_rebuttal_pairs(forums, guest_client)
with corenlp.CoreNLPClient(annotators=CORENLP_ANNOTATORS,
output_format='conll') as corenlp_client:
return orl.get_classification_examples(pairs, "review", sid_map,
corenlp_client)
def testPart(): TEST_SENTENCE = "Which prize did Frederick Buechner create?" with corenlp.CoreNLPClient(annotators='tokenize ssplit parse lemma pos ner'.split()) as testclient: c = testclient.annotate(TEST_SENTENCE) TEST_PARSE_TREE = c.sentence[0].parseTree TEST_Q = queue.Queue(maxsize=MAX_SIZE) TEST_Q.put(TEST_PARSE_TREE) print (TEST_PARSE_TREE) tokens_list = parse_tree_WHNP(TEST_Q) print (tokens_list) spanList = generate_candidateAnswerSpans(tokens_list, c.sentence[0].token) sizeList = len(spanList) for s in spanList: print (str(s.tokens) + str(s.type)) return 0
def __init__(self):
directory = (os.getcwd() + r'\\corenlp')
os.environ['CORENLP_HOME'] = directory
prop = {'pos.model': directory + r'\gate-EN-twitter.model'}
self.client = corenlp.CoreNLPClient(
annotators="tokenize pos lemma".split(),
properties=prop,
output_format='json')
#to combat connection error which occur in initial trial
for _ in range(3):
try:
self.client.annotate('Hello, World')
break
except:
continue
def annotate(sentence, lower=True):
global client
if client is None:
client = corenlp.CoreNLPClient(annotators='ssplit,tokenize'.split(','))
words, gloss, after = [], [], []
for s in client.annotate(sentence).sentence:
for t in s.token:
words.append(t.word)
gloss.append(t.originalText)
after.append(t.after)
if lower:
words = [w.lower() for w in words]
return {
'gloss': gloss,
'words': words,
'after': after,
}
def separate_sentences(keywords, filepath, mode=0):
global out_file
pattern = ''
for word in keywords:
pattern += '(' + word + ')|'
pattern = pattern[:-1]
# print(pattern)
# return
# pattern += ']'
# print(pattern)
with corenlp.CoreNLPClient(annotators="tokenize ssplit".split()) as client:
with open(filepath) as file:
i = 0
text = ''
headline = False
for line in file:
if i < 2:
if i == 0 and re.search(pattern, line, re.IGNORECASE):
headline = True
out_file.write(line)
i += 1
continue
if line == '\n':
file.readline()
# print(text)
ann = client.annotate(text)
# print('\n\n\n')
# print('=================================================================')
for i in range(len(ann.sentence)):
# print(corenlp.to_text(ann.sentence[i]))
sentence = corenlp.to_text(ann.sentence[i])
if headline and mode == 0:
out_file.write(sentence + '\n')
elif re.search(pattern, sentence, re.IGNORECASE):
out_file.write(sentence + '\n')
out_file.write('\n\n')
# print('=================================================================')
# print('\n\n\n')
text = ''
headline = False
i = 0
continue
text += line
def corenlp_client(self):
if self._corenlp_client is None:
if not os.environ.get('CORENLP_HOME'):
os.environ['CORENLP_HOME'] = os.path.abspath(
os.path.join(
os.path.dirname(__file__),
'../../third_party/stanford-corenlp-full-2018-10-05'))
if not os.path.exists(os.environ['CORENLP_HOME']):
raise Exception(
'''Please install Stanford CoreNLP and put it at {}.
Direct URL: http://nlp.stanford.edu/software/stanford-corenlp-full-2018-10-05.zip
Landing page: https://stanfordnlp.github.io/CoreNLP/'''.
format(os.environ['CORENLP_HOME']))
self._corenlp_client = corenlp.CoreNLPClient(
annotators="tokenize ssplit")
return self._corenlp_client
def tokenize_lemmatize(data):
import corenlp
os.environ['CORENLP_HOME'] = 'lib/stanford-corenlp-full'
with corenlp.CoreNLPClient(annotators='tokenize ssplit pos lemma'.split()) as client:
for sample in data:
ann = client.annotate(replace_email(replace_phone(replace_url(sample['text']))))
tokens = []
lemmas = []
pos = []
for sent in ann.sentence:
tokens += [format_token(token.word) for token in sent.token]
lemmas += [format_token(token.lemma) for token in sent.token]
pos += [token.pos for token in sent.token]
sample['tokens'] = tokens
sample['lemmas'] = lemmas
sample['pos'] = pos
return data
def __init__(self,
model="stanford-corenlp-full-2018-10-05",
lemmatize=False):
if not os.environ.get('CORENLP_HOME'):
os.environ['CORENLP_HOME'] = os.path.abspath(
os.path.join(os.path.dirname(__file__),
f'../../third_party/{model}'))
if not os.path.exists(os.environ['CORENLP_HOME']):
raise Exception(
f'''Please install Stanford CoreNLP and put it at {os.environ['CORENLP_HOME']}.
Direct URL: http://nlp.stanford.edu/software/stanford-corenlp-full-2018-10-05.zip
command: `curl https://nlp.stanford.edu/software/stanford-corenlp-full-2018-10-05.zip | jar xv`
Landing page: https://stanfordnlp.github.io/CoreNLP/''')
self.client = corenlp.CoreNLPClient()
self.corenlp_annotators = ['tokenize', 'ssplit']
if lemmatize:
self.corenlp_annotators.append('lemma')
def main():
args = parser.parse_args()
conn = ordb.create_connection(args.dbfile)
if conn is not None:
ordb.create_table(conn, ordb.CREATE_COMMENTS_TABLE)
else:
print("Error! cannot create the database connection.")
conn = ordb.create_connection(args.dbfile)
with corenlp.CoreNLPClient(annotators=ANNOTATORS,
output_format='conll') as corenlp_client:
orl.get_datasets(args.inputfile,
corenlp_client,
conn,
debug=args.debug)
metadata = create_metadata_json(conn)
relevant_text = create_text_json(conn)
def do_annotate(args):
args.props = dict(args.props) if args.props else {}
if args.sentence_mode:
args.props["ssplit.isOneSentence"] = True
with corenlp.CoreNLPClient(annotators=args.annotators,
properties=args.props,
be_quiet=not args.verbose_server) as client:
for line in args.input:
if line.startswith("#"): continue
ann = client.annotate(line.strip(), output_format=args.format)
if args.format == "json":
if args.sentence_mode:
ann = ann["sentences"][0]
args.output.write(json.dumps(ann))
args.output.write("\n")
def main():
guest_client = openreview.Client(baseurl='https://api.openreview.net')
conference = orl.Conference.iclr18
notes = list(openreview.tools.iterget_notes(
guest_client,
invitation=orl.INVITATION_MAP[conference]))
obj = {conference:[]}
with corenlp.CoreNLPClient(
annotators=orl.CORENLP_ANNOTATORS,
output_format='conll') as corenlp_client:
for note in notes:
p = orl.get_tokenized_chunks(corenlp_client, note.content["abstract"])
obj[conference].append(p)
with open("tokenized_abstracts.json", 'w') as f:
json.dump(obj, f)
