alexbowe · March 21, 2011 12:59 · aciong32 · Feb 19, 2016 · anupamchoudhari · Feb 23, 2016
diff --git a/nltk-intro.py b/nltk-intro.py
 import nltk

 text = """The Buddha, the Godhead, resides quite as comfortably in the circuits of a digital
 computer or the gears of a cycle transmission as he does at the top of a mountain
 or in the petals of a flower. To think otherwise is to demean the Buddha...which is
 to demean oneself."""

 # Used when tokenizing words
 sentence_re = r'''(?x)      # set flag to allow verbose regexps
      ([A-Z])(\.[A-Z])+\.?  # abbreviations, e.g. U.S.A.
    | \w+(-\w+)*            # words with optional internal hyphens
    | \$?\d+(\.\d+)?%?      # currency and percentages, e.g. $12.40, 82%
    | \.\.\.                # ellipsis
    | [][.,;"'?():-_`]      # these are separate tokens
 '''

 lemmatizer = nltk.WordNetLemmatizer()
 stemmer = nltk.stem.porter.PorterStemmer()

 #Taken from Su Nam Kim Paper...
 grammar = r"""
    NBAR:
        {<NN.*|JJ>*<NN.*>}  # Nouns and Adjectives, terminated with Nouns
        
    NP:
        {<NBAR>}
        {<NBAR><IN><NBAR>}  # Above, connected with in/of/etc...
 """
 chunker = nltk.RegexpParser(grammar)

 toks = nltk.regexp_tokenize(text, sentence_re)
 postoks = nltk.tag.pos_tag(toks)

 print postoks

 tree = chunker.parse(postoks)

 from nltk.corpus import stopwords
 stopwords = stopwords.words('english')


 def leaves(tree):
    """Finds NP (nounphrase) leaf nodes of a chunk tree."""
    for subtree in tree.subtrees(filter = lambda t: t.node=='NP'):
        yield subtree.leaves()

 def normalise(word):
    """Normalises words to lowercase and stems and lemmatizes it."""
    word = word.lower()
    word = stemmer.stem_word(word)
    word = lemmatizer.lemmatize(word)
    return word

 def acceptable_word(word):
    """Checks conditions for acceptable word: length, stopword."""
    accepted = bool(2 <= len(word) <= 40
        and word.lower() not in stopwords)
    return accepted


 def get_terms(tree):
    for leaf in leaves(tree):
        term = [ normalise(w) for w,t in leaf if acceptable_word(w) ]
        yield term

 terms = get_terms(tree)

 for term in terms:
    for word in term:
        print word,
    print
	import nltk

	text = """The Buddha, the Godhead, resides quite as comfortably in the circuits of a digital
	computer or the gears of a cycle transmission as he does at the top of a mountain
	or in the petals of a flower. To think otherwise is to demean the Buddha...which is
	to demean oneself."""

	# Used when tokenizing words
	sentence_re = r'''(?x) # set flag to allow verbose regexps
	([A-Z])(\.[A-Z])+\.? # abbreviations, e.g. U.S.A.
	\| \w+(-\w+)* # words with optional internal hyphens
	\| \$?\d+(\.\d+)?%? # currency and percentages, e.g. $12.40, 82%
	\| \.\.\. # ellipsis
	\| [][.,;"'?():-_`] # these are separate tokens
	'''

	lemmatizer = nltk.WordNetLemmatizer()
	stemmer = nltk.stem.porter.PorterStemmer()

	#Taken from Su Nam Kim Paper...
	grammar = r"""
	NBAR:
	{<NN.\|JJ><NN.*>} # Nouns and Adjectives, terminated with Nouns

	NP:
	{<NBAR>}
	{<NBAR><IN><NBAR>} # Above, connected with in/of/etc...
	"""
	chunker = nltk.RegexpParser(grammar)

	toks = nltk.regexp_tokenize(text, sentence_re)
	postoks = nltk.tag.pos_tag(toks)

	print postoks

	tree = chunker.parse(postoks)

	from nltk.corpus import stopwords
	stopwords = stopwords.words('english')


	def leaves(tree):
	"""Finds NP (nounphrase) leaf nodes of a chunk tree."""
	for subtree in tree.subtrees(filter = lambda t: t.node=='NP'):
	yield subtree.leaves()

	def normalise(word):
	"""Normalises words to lowercase and stems and lemmatizes it."""
	word = word.lower()
	word = stemmer.stem_word(word)
	word = lemmatizer.lemmatize(word)
	return word

	def acceptable_word(word):
	"""Checks conditions for acceptable word: length, stopword."""
	accepted = bool(2 <= len(word) <= 40
	and word.lower() not in stopwords)
	return accepted


	def get_terms(tree):
	for leaf in leaves(tree):
	term = [ normalise(w) for w,t in leaf if acceptable_word(w) ]
	yield term

	terms = get_terms(tree)

	for term in terms:
	for word in term:
	print word,
	print