def run_relation_annotator(self, doctag, annotator):
"""
Classify a document using an annotator and insert results into the database
:param doctag: tag of the document
:param annotator: annotator to classify
:return:
"""
# process whole document instead of sentence by sentence
sentences = self.get_sentences(doctag)
data = bottle.request.json
output = {}
for a in self.relation_annotators: # a in (annotator_name, annotator_engine, annotator_etype)
if a[0] == annotator:
input_sentences = []
for s in sentences:
sentence = Sentence(s[2], offset=s[3], sid=s[1], did=doctag)
sentence.process_corenlp_output(ast.literal_eval(s[4]))
sentence = self.get_entities(sentence)
input_sentences.append(sentence)
sentence_results = self.relation_annotators[a].annotate_sentences(input_sentences)
for sentence in input_sentences:
if a[1] == "jsre":
pred, original = sentence_results[s[1]]
sentence_relations = self.relation_annotators[a].process_sentence(pred, original, sentence)
elif a[1] == "smil":
sentence_relations = self.relation_annotators[a].process_sentence(sentence)
for p in sentence_relations:
self.add_relation(p, annotator)
output[p.pid] = str(p)
return json.dumps(output)
def run_entity_annotator(self, doctag, annotator):
"""
Classify a document using an annotator and insert results into the database
:param doctag: tag of the document
:param annotator: annotator to classify
:return:
"""
sentences = self.get_sentences(doctag)
data = bottle.request.json
output = {}
for a in self.entity_annotators: # a in (annotator_name, annotator_engine, annotator_etype)
if a[0] == annotator:
for s in sentences:
sentence = Sentence(s[2], offset=s[3], sid=s[1], did=doctag)
#sentence.process_sentence(self.corenlp)
sentence.process_corenlp_output(ast.literal_eval(s[4]))
sentence_text = " ".join([t.text for t in sentence.tokens])
sentence_output = self.entity_annotators[a].annotate_sentence(sentence_text)
#print sentence_output
sentence_entities = self.entity_annotators[a].process_sentence(sentence_output, sentence)
for e in sentence_entities:
sentence_entities[e].normalize()
self.add_entity(sentence_entities[e], annotator)
output[e] = str(sentence_entities[e])
# print output
return json.dumps(output)
def get_document(self, doctag):
# return document entry with doctag
cur = self.db_conn.cursor()
query = """SELECT distinct id, doctag, title, doctext
FROM document
WHERE doctag =%s;"""
# print "QUERY", query
cur.execute(query, (doctag, ))
res = cur.fetchone()
if res is not None:
result = {
'docID': res[1],
'title': res[2],
'docText': res[3],
'abstract': {
'sentences': []
}
}
sentences = self.get_sentences(doctag)
for s in sentences:
sentence = Sentence(s[2], offset=s[3], sid=s[1], did=doctag)
sentence.process_corenlp_output(ast.literal_eval(s[4]))
sentence = self.get_entities(sentence)
result['abstract']['sentences'].append(sentence.get_dic("all"))
output = json.dumps(result)
return output
else:
return json.dumps(
{'error': 'could not find document {}'.format(doctag)})
def get_document(self, doctag):
# return document entry with doctag
cur = self.db_conn.cursor()
query = """SELECT distinct id, doctag, title, doctext
FROM document
WHERE doctag =%s;"""
# print "QUERY", query
cur.execute(query, (doctag,))
res = cur.fetchone()
if res is not None:
result = {'docID': res[1], 'title': res[2], 'docText': res[3], 'abstract':{'sentences':[]}}
sentences = self.get_sentences(doctag)
for s in sentences:
sentence = Sentence(s[2], offset=s[3], sid=s[1], did=doctag)
sentence.process_corenlp_output(ast.literal_eval(s[4]))
sentence = self.get_entities(sentence)
result['abstract']['sentences'].append(sentence.get_dic("all"))
output = json.dumps(result)
return output
else:
return json.dumps({'error': 'could not find document {}'.format(doctag)})
