def nlp_nlidb(question):
'''STEP1: Extracting the keywords using Parser:'''
tree = stanford_client.to_tree(question)
top_node = penn_treebank_node.parse(tree)
extracted_words, Proper_Nouns = my_parser.key_words(top_node, question)
question_type = my_parser.questionType(top_node)
'''STEP2: Replace words with glossary terms:'''
uniqueWords = glossary.generalizedKeywords(question, extracted_words)
# '''STEP2.2: Extracting the related keywords:'''
uniqueWords.append(question_type)
'''STEP3: Adding some manually defined rules'''
allWords, conditions, target, component_value = answerGenerator(question, uniqueWords)
# To be handled differently, this is here only so the master branch can run.
allWords = filter(lambda x: x != None, allWords)
allWords = allWords + list(target)
allWords = set(allWords)
'''Creating Links between allWords and tables' entities'''
tables = set(semanticNet.tables(allWords))
'''required_values is not being used in our system anymore'''
required_values =''
debug.debug_statement([allWords, required_values, target, conditions, tables, question_type, Proper_Nouns, component_value])
return allWords, required_values, target, conditions, tables, question_type, Proper_Nouns, component_value
def _formatKeyWords(keyWords):
nouns = []
verbs = []
adjs_prpos = []
to_remove = []
extracted_words = []
# PPN = []
# Check that at least 1 keyword was found.
all_keywords = [item for sublist in keyWords.values() for item in sublist]
if len(all_keywords) < 1: debug.debug_statement("No keywords found!")
else:
for n in keyWords['Nouns']:
if n!= None:
# n_lemmatized = WN_Lemmatizer().lemmatize(n.word)
# nouns.append(str(n_lemmatized))
nouns.append(str(n.word))
for v in keyWords['Verbs']:
"remove the auxiliary verb 'to be':"
if en.verb.infinitive(v) == 'be':
to_remove.append(v)
if v!= None:
# v_stemmed = PStemmer().stem(v.word)
# verbs.append(str(v_stemmed))
verbs.append(str(v.word))
for adj in keyWords['Adjectives and Propositions']:
if adj!= None:
adjs_prpos.append(str(adj.word))
# for pr_n in keyWords['Proper Nouns']:
# if pr_n != None:
# PPN.append(pr_n)
verbs = [x for x in verbs if x not in to_remove]
'''combine all key words extracted for each category:'''
extracted_words = nouns + verbs + adjs_prpos
while '' in extracted_words:
extracted_words.remove('')
# print extracted_words
return extracted_words