def __init__(self,
bigram_count_file_name,
bigram_model_pickle='bigram_model.pickle'):
self.dictionary = ourdict.dictionary
self.tokenizer = Tokenizer()
with open(bigram_count_file_name, 'r') as f:
self.bigram_model = LaplaceBigramModel(f)
with open('windows1.txt', 'r') as f:
self.unigram_model = LaplaceUnigramModel(f)
def __init__(self, bigram_count_file_name, bigram_model_pickle='bigram_model.pickle'):
self.dictionary = ourdict.dictionary
self.tokenizer = Tokenizer()
with open(bigram_count_file_name, 'r') as f:
self.bigram_model = LaplaceBigramModel(f)
with open('windows1.txt', 'r') as f:
self.unigram_model = LaplaceUnigramModel(f)
class Translator:
def __init__(self,
bigram_count_file_name,
bigram_model_pickle='bigram_model.pickle'):
self.dictionary = ourdict.dictionary
self.tokenizer = Tokenizer()
with open(bigram_count_file_name, 'r') as f:
self.bigram_model = LaplaceBigramModel(f)
with open('windows1.txt', 'r') as f:
self.unigram_model = LaplaceUnigramModel(f)
def translate_sentence(self, sentence):
###################################################################################
# Call PRE-processing rules as functions of sentence and returning sentences HERE #
###################################################################################
tokens = self.tokenizer.tokenize(sentence)
###############################################################
# or as functions of token list and returning toke list HERE. #
tokens = self.remove_se(tokens)
###############################################################
translated_tokens = ['^'] # Arbitrary start
for i in range(0, len(tokens)):
token = tokens[i].lower()
if (token == "para"):
translated_tokens.append(
self.para_process(token, tokens[i + 1]))
else:
translated_tokens.append(
self.find_next_word(token, translated_tokens))
# for token in tokens:
# token = token.lower()
# translated_tokens.append(self.find_next_word(token, translated_tokens))
#######################################################################################
# Call POST-processing rules as functions of token list and returning token list HERE #
#######################################################################################
translation = self.format(translated_tokens)
###########################################################
# or as functions of sentence and returning sentence HERE #
translation = self.reverse_noun_adj([translation]) #
###########################################################
return translation
def para_process(self, para, next_word):
if (len(next_word) > 1):
suffix = next_word[len(next_word) - 2:]
print "suffix: %s" % suffix
if suffix == 'ar' or suffix == 'er' or suffix == 'ir':
return 'to'
else:
return 'for'
return 'for'
def find_next_word(self, word, current_translation):
candidate_words = self.dictionary[word]
top_score = float("-inf")
prev_word = current_translation[-1]
if (prev_word == ',') or (prev_word == '.'):
prev_word = current_translation[
-2] # If the previous token is punctuation, get what's before it
for word in candidate_words:
# score = self.bigram_model.score([prev_word, word])
score = self.bigram_model.score(
[prev_word, word]) + self.unigram_model.score([word])
if (score > top_score):
best = word
top_score = score
return best
def format(self, token_list):
''' takes the list of translated words and formats it nicely for printing '''
s = " ".join(
token_list[1:]
) # Remove the leading start token and turn into a spaced string
s = re.sub(r' ([\.,])', r'\1',
s) # Remove whitespace before punctuation
s = s[0].upper() + s[1:] # Capitalize the sentence
return s
###########################################################
# ADD YOUR PREPROCESSING + POSTPROCESSING FUNCTIONS HERE. #
###########################################################
def reverse_noun_adj(self, s):
noun_tags = set(['NNP', 'NN', 'NNS'])
adj_tags = set(['JJ'])
parsed = parse.parse_english(s)[0]
# print parsed
words = parsed.words
for i in range(len(words) - 1):
if parsed.tags[i] in noun_tags:
if parsed.tags[i + 1] in adj_tags:
w = words[i]
words[i] = words[i + 1]
words[i + 1] = w
print ">>>> SWITCHED %s and %s" % (w, words[i])
words = ['^'] + words # stupid hack to make the formatting work
# print words
s = self.format(words)
return s
def remove_se(self, spanish_tokens):
new_tokens = []
for t in spanish_tokens:
if t != "se":
new_tokens.append(t)
else:
new_tokens.append("usted")
return new_tokens
class Translator:
def __init__(self, bigram_count_file_name, bigram_model_pickle='bigram_model.pickle'):
self.dictionary = ourdict.dictionary
self.tokenizer = Tokenizer()
with open(bigram_count_file_name, 'r') as f:
self.bigram_model = LaplaceBigramModel(f)
with open('windows1.txt', 'r') as f:
self.unigram_model = LaplaceUnigramModel(f)
def translate_sentence(self, sentence):
#PRE-processing#
tokens = self.tokenizer.tokenize(sentence)
tokens = self.remove_se(tokens)
#Processing#
translated_tokens = ['^'] # Arbitrary start
for i in range(0, len(tokens)):
token = tokens[i].lower()
if (token == "para"):
translated_tokens.append(self.para_process(token, tokens[i + 1]))
else:
translated_tokens.append(self.find_next_word(token, translated_tokens))
#POST-processing#
translation = self.format(translated_tokens)
return translation
def para_process(self, para, next_word):
if (len(next_word) > 1):
suffix = next_word[len(next_word)-2:]
print "suffix: %s" % suffix
if suffix == 'ar' or suffix == 'er' or suffix == 'ir':
return 'to'
else:
return 'for'
return 'for'
def find_next_word(self, word, current_translation):
candidate_words = self.dictionary[word]
top_score = float("-inf")
prev_word = current_translation[-1]
if (prev_word == ',') or (prev_word == '.'):
prev_word = current_translation[-2] # If the previous token is punctuation, get what's before it
for word in candidate_words:
score = self.bigram_model.score([prev_word, word]) + self.unigram_model.score([word])
if (score > top_score):
best = word
top_score = score
return best
def format(self, token_list):
''' takes the list of translated words and formats it nicely for printing '''
s = " ".join(token_list[1:]) # Remove the leading start token and turn into a spaced string
s = re.sub(r' ([\.,])', r'\1', s) # Remove whitespace before punctuation
s = s[0].upper() + s[1:] # Capitalize the sentence
return s
def reverse_noun_adj(self, s):
noun_tags = set(['NNP', 'NN', 'NNS'])
adj_tags = set(['JJ'])
parsed = parse.parse_english(s)[0]
words = parsed.words
for i in range(len(words)-1):
if parsed.tags[i] in noun_tags:
if parsed.tags[i+1] in adj_tags:
w = words[i]
words[i] = words[i+1]
words[i+1] = w
words = ['^'] + words # makes the formatting work
s = self.format(words)
return s
def remove_se(self, spanish_tokens):
new_tokens = []
for t in spanish_tokens:
if t != "se":
new_tokens.append(t)
else:
new_tokens.append("usted")
return new_tokens