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'''
The library for natural language processing, and categorizing
information using NLTK
'''
import nltk
from nltk import tokenize
import math
import os
class InputBreaker:
def __init__(self, text):
self.fSentences = []
self.qEntitySentences = []
self.analyzeText(text)
#print self.qEntitySentences
def analyzeText(self, text):
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
sents = tokenizer.tokenize(text)
for sentence in sents:
self.determineSentenceType(sentence)
def determineSentenceType(self, sentence):
words = nltk.word_tokenize(sentence)
tagit = nltk.pos_tag(words)
#print tagit
if (tagit[0][1] == 'NNP' and tagit[-1][1] == 'PRP') or (tagit[0][1] == 'WP' and tagit[-1][1] == 'NN'):
self.qEntitySentences.append(tagit)
class Match:
def __init__(self, questions):
#print questions
self.file = open("./FAQ/sugar.txt","rb")
self.mostprobableQ = ""
self.tomatchwords = []
for question in questions:
self.findOptimalQuestion(question)
def findOptimalQuestion(self, question):
i = 0
listofmetricsforsentence = []
self.tomatchwords = self.extractKeywords(question)
#print self.tomatchwords
for line in self.file.readlines():
linetosave = line
line = line.lower()
if line[0:2] == 'q.':
successmeter = 0
for word in self.tomatchwords:
if line.find(word) != -1:
successmeter+=1
else:
pass
if successmeter > 0:
listofmetricsforsentence.append([self.tomatchwords, successmeter, linetosave, ])
#print listofmetricsforsentence
self.mostprobableQ = self.doMath(listofmetricsforsentence)[2]
print self.mostprobableQ
self.file.close()
def doMath(self,metrics):
greatestmetric = []
metricno = 10
for metric in metrics:
userqtags = metric[0]
success = metric[1]
words = nltk.word_tokenize(metric[2].split('Q.')[1])
tagit = nltk.pos_tag(words)
databaseqtags = self.extractKeywords(tagit)
diff = math.sqrt((len(databaseqtags) - len(userqtags))*(len(databaseqtags) - len(userqtags)))
if diff > success:
tocheck = diff - success
else:
tocheck = success - diff
if tocheck < metricno or (success < diff and metricno != 0 and success > 0):
metricno = tocheck
greatestmetric = metric
print metricno
print greatestmetric
return greatestmetric
def extractKeywords(self, question):
self.tomatchwords = []
print question
for tag in question:
if tag[1] == "NN" or tag[1] == "JJ" or tag[1] == "NNP":
self.tomatchwords.append(tag[0])
print self.tomatchwords
return self.tomatchwords
def reportAnswer(self,):
self.file = open("./FAQ/sugar.txt","rb")
content = self.file.read()
tuple = content.split(self.mostprobableQ)
self.file.close()
return tuple[1].split('\n')[1]
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