fixed unit test bugs for wsd

nltk/test/wsd.doctest

@@ -17,22 +17,20 @@ a the definitions of the ambiguous word.
 
 Given an ambiguous word and the context in which the word occurs, Lesk returns 
 a Synset with the highest number of overlapping words between the context 
-sentence and different definitions form each Synset.
+sentence and different definitions from each Synset.
 
-        >>> from nltk.wsd import lesk
+    >>> from nltk.wsd import lesk
 	>>> sent = "I went to the bank to deposit money."
 	>>> word = "bank"
 	>>> pos = "n"
-	>>> print lesk(sent, word, pos)
+	>>> print(lesk(sent, word, pos))
 	Synset('depository_financial_institution.n.01')
 
 The definitions for "bank" are:
 
 	>>> from nltk.corpus import wordnet as wn
 	>>> for ss in wn.synsets('bank'):
-	...     print ss, ss.definition()
-	... 
-	
+	...     print(ss, ss.definition())
 	Synset('bank.n.01') sloping land (especially the slope beside a body of water)
 	Synset('depository_financial_institution.n.01') a financial institution that accepts deposits and channels the money into lending activities
 	Synset('bank.n.03') a long ridge or pile

nltk/wsd.py

@@ -43,11 +43,11 @@ def lesk(context_sentence, ambiguous_word, pos=None, dictionary=None):
         >>> sent = word_tokenize("I went to the bank to deposit money.")
         >>> word = "bank"
         >>> pos = "n"
-        >>> wsd(sent, word, pos)
+        >>> lesk(sent, word, pos)
         Synset('depository_financial_institution.n.01')
     
     :param context_sentence: The context sentence where the ambiguous word occurs.
-    :param ambiguous: The ambiguous word that requires WSD.
+    :param ambiguous_word: The ambiguous word that requires WSD.
     :param pos: A specified Part-of-Speech (POS).
     :param dictionary: A list of words that 'signifies' the ambiguous word.
     :return: ``lesk_sense`` The Synset() object with the highest signature overlaps.
@@ -56,7 +56,7 @@ def lesk(context_sentence, ambiguous_word, pos=None, dictionary=None):
         dictionary = {}
         for ss in wn.synsets(ambiguous_word):
             dictionary[ss] = ss.definition().split()
-    best_sense = _compare_overlaps_greedy(context_sentence, \
+    best_sense = _compare_overlaps_greedy(context_sentence,
                                        dictionary, pos)
     return best_sense