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