old_contents: # LING 573 Question Answering System
  # Code last updated 4/15/14 by Andrea Kahn
  #
  # This code implements a QueryProcessor for the question answering system.
  
  
  import sys
  import general_classes
  import nltk
  
  # TODO: A QueryProcessor should be initialized with the Question object, but should it
  # have this question as an attribute, or should it have attributes id, type, q, target that
  # match those of the question?
  # Advantage of first approach: allows us to change the Question and have the QueryProcessor
  # update automatically (not sure we'd ever do this).
  # Advantage of second approach: saves typing (access the id with self.id, versus self.question.id).
  
  # A QueryProcessor object has the attribute "question", a Question object.
  
  class QueryProcessor(object):
  	def __init__(self, question):
  		self.question = question
  
  	# This method returns a set of SearchQuery objects.
  	def generate_queries(self):
          tokenized_q = nltk.word_tokenize(self.question.q)
          tokenized_target = nltk.word_tokenize(self.question.target)
  		# FIXME: Strip out punctuation tokens
          # note from Claire: here is a temporary fix
          punctuation = ['?','.',',']
          search_query = [x for x in tokenized_q if x not in punctuation] + [x for x in tokenized_target if x not in punctuation]
  		# FIXME: Issue with leading escape character in some questions 
          return [search_query]
  	
  	# This method returns an AnswerTemplate object.
  	def generate_ans_template(self):
  	
  		# NB: The following if statement should always evaluate as True in our system, but
  		# its inclusion enables the system to more easily be extended to handle other types
  		# of questions, for which the text-processing and AnswerTemplate-generation steps
  		# might be slightlydifferent.
  
  		if self.question.type=="FACTOID":		
  			# do some sort of text-processing on the natural-language question and context
  			# to determine NE type
  			# generate a corresponding AnswerTemplate object
  			# return it
  			return None
  
  		else:
  			sys.stderr.write("Warning: System can only handle \"factoid\" questions\n")
  
new_contents: # LING 573 Question Answering System
  # Code last updated 4/15/14 by Andrea Kahn
  #
  # This code implements a QueryProcessor for the question answering system.
  
  
  import sys
  import general_classes
  import nltk
  
  # TODO: A QueryProcessor should be initialized with the Question object, but should it
  # have this question as an attribute, or should it have attributes id, type, q, target that
  # match those of the question?
  # Advantage of first approach: allows us to change the Question and have the QueryProcessor
  # update automatically (not sure we'd ever do this).
  # Advantage of second approach: saves typing (access the id with self.id, versus self.question.id).
  
  # A QueryProcessor object has the attribute "question", a Question object.
  
  class QueryProcessor(object):
      def __init__(self, question):
          self.question = question
  
  	# This method returns a set of SearchQuery objects.
      def generate_queries(self):
          tokenized_q = nltk.word_tokenize(self.question.q)
          tokenized_target = nltk.word_tokenize(self.question.target)
  		# FIXME: Strip out punctuation tokens
          # note from Claire: here is a temporary fix
          punctuation = ['?','.',',']
          search_query = [x for x in tokenized_q if x not in punctuation] + [x for x in tokenized_target if x not in punctuation]
  		# FIXME: Issue with leading escape character in some questions 
          return [search_query]
  	
  	# This method returns an AnswerTemplate object.
  	def generate_ans_template(self):
  	
  		# NB: The following if statement should always evaluate as True in our system, but
  		# its inclusion enables the system to more easily be extended to handle other types
  		# of questions, for which the text-processing and AnswerTemplate-generation steps
  		# might be slightlydifferent.
  
  		if self.question.type=="FACTOID":		
  			# do some sort of text-processing on the natural-language question and context
  			# to determine NE type
  			# generate a corresponding AnswerTemplate object
  			# return it
  			return None
  
  		else:
  			sys.stderr.write("Warning: System can only handle \"factoid\" questions\n")
  
subject: Fix silly Python indentation issues, again.