def get_tagged_from_server(input_text):
"""
Send the input_text to the CoreNLP server and retrieve the tokens, named entity tags and part-of-speech tags.
"""
corenlp_output = nlp_corenlp.annotate(input_text, properties=corenlp_properties).get("sentences", [])[0]
tagged = [(t['originalText'], t['ner'], t['pos']) for t in corenlp_output['tokens']]
return tagged
def predict_openie():
request.get_json(force=True)
try:
if request.method == 'POST':
text = request.json.get("inputtext")
output = nlp_corenlp.annotate(text,
properties={
'annotators': 'dcoref',
'outputFormat': 'json',
'ner.useSUTime': 'false'
})
resolve(output)
coreftext = get_resolved(output)
output = nlp_corenlp.annotate(coreftext,
properties={
'annotators': 'openie',
'outputFormat': 'json',
'ner.useSUTime': 'false'
})
out = list(map(lambda x: x['openie'], output['sentences']))
flat_list = [item for sublist in out for item in sublist]
dfnc, entdf, desiglist = nlpent.ner(coreftext)
dfnc = dfnc.to_dict(orient="records")
entdf = entdf.to_dict(orient="records")
keyconcepts = pd.DataFrame(list(getkeys(text)))
keyconcepts.columns = ["Score", "Keywords"]
keyconcepts = keyconcepts.to_dict(orient="records")
return jsonify({
"ie": flat_list,
"dfnc": dfnc,
"entdf": entdf,
"kc": keyconcepts,
"ds": desiglist
})
else:
return "No result"
except Exception as e:
print(str(e))
return jsonify({"ie": "No relation detected"})
def stan_core(self, text):
try:
output = nlp_corenlp.annotate(text,
properties={
'annotators': 'dcoref',
'outputFormat': 'json',
'ner.useSUTime': 'false'
})
self.resolve(output)
sent_no_articles = self.get_resolved(output)
sent_no_articles
except:
sent_no_articles = text
return sent_no_articles