import nltk
from nltk.stem.lancaster import LancasterStemmer
from nltk.corpus import stopwords, brown
from text_processor import TextProcessor
from keyword_retrieval import KeywordRetrieval
from datetime import datetime
if __name__ == "__main__":
text = raw_input("Please input your text (It's better not to input more than 15 words)\n>> ")
# case based ontology
print "Case: [[ Ontology-based method]]\nText :%s" % text
# create text processor to get keywords
start = datetime.now()
text_processor = TextProcessor(text = text)
keywords = text_processor.get_keywords()
if keywords:
dos = {}
print "Extracted ontology keywords:", keywords
for word in keywords:
kr = KeywordRetrieval(keyword=word)
dos[word] = kr.get_result()
else:
print "The model does not extract any keywords"
end = datetime.now()
time_1 = end - start
for word in keywords:
if dos[word]:
# sorted by name's length(similarity)
for do in sorted(dos[word], key=lambda do: len(do.name)):
# print do's information
print "_"*100
