def extractNP(CONTENT):
stopwords = getList()
grammer = r"""
NBAR:
{<NN.*|JJ>*<NN.*>}
NP:
{<NBAR>}
{<NBAR><IN><NBAR>}
"""
chunker = nltk.RegexpParser(grammer) # create a chunker with the parser
# sentences = nltk.sent_tokenize(CONTENT)
lemmaobj = WordNetLemmatizer()
words = []
paragraphs = [p for p in CONTENT.split('\n') if p]
for para in paragraphs:
sentences = [s for s in nltk.sent_tokenize(para) if s]
for sentence in sentences:
word = [w.lower() for w in nltk.word_tokenize(sentence)]
taggedwords = nltk.pos_tag(word)
tree = chunker.parse(taggedwords)
temp = []
for subtree in tree.subtrees():
if subtree.label() == "NP":
for leaves in subtree.leaves():
w = leaves[0].lower()
if w not in stopwords:
w = lemmaobj.lemmatize(w)
temp.append(w)
if temp != []:
words.append(temp)
temp = []
return words
def extractNP(CONTENT):
stopwords = getList()
grammer = r"""
NBAR:
{<NN.*|JJ>*<NN.*>}
NP:
{<NBAR>}
{<NBAR><IN><NBAR>}
"""
chunker = nltk.RegexpParser(grammer) # create a chunker with the parser
# sentences = nltk.sent_tokenize(CONTENT)
lemmaobj = WordNetLemmatizer()
words = []
paragraphs = [p for p in CONTENT.split('\n') if p]
for para in paragraphs:
sentences = [s for s in nltk.sent_tokenize(para) if s]
for sentence in sentences:
word = [w.lower() for w in nltk.word_tokenize(sentence)]
taggedwords = nltk.pos_tag(word)
tree = chunker.parse(taggedwords)
temp = []
for subtree in tree.subtrees():
if subtree.label() == "NP":
for leaves in subtree.leaves():
w = leaves[0].lower()
if w not in stopwords:
w = lemmaobj.lemmatize(w)
temp.append(w)
if temp!=[]:
words.append(temp)
temp = []
return words
import operator
import sys
from textblob import TextBlob
from rake import RakeKeywordExtractor
from textblob.np_extractors import ConllExtractor
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import WordPunctTokenizer
from textblob.taggers import NLTKTagger
from stopwordList import getList
import codecs
## GLOBAL VARIABLES
top_fraction = 1
LEMMA_OBJ = WordNetLemmatizer()
tokenizer = WordPunctTokenizer()
nltk_tagger = NLTKTagger()
stopwords = getList()
COLL_OBJ = ConllExtractor()
def extractKeywords(phrase_list):
RAKE_OBJ = RakeKeywordExtractor(set([]))
word_scores = RAKE_OBJ._calculate_word_scores(phrase_list)
phrase_scores = RAKE_OBJ._calculate_phrase_scores(phrase_list, word_scores)
sorted_phrase_scores = sorted(phrase_scores.iteritems(),
key=operator.itemgetter(1),
reverse=True)
n_phrases = len(sorted_phrase_scores)
return [x[0] for x in sorted_phrase_scores[0:int(n_phrases)]]
def extractChunks(CONTENT):
def __init__(self,additional_stopwords): #self.stopwords = set(nltk.corpus.stopwords.words()) self.stopwords = set(getList()) self.stopwords = self.stopwords | additional_stopwords self.top_fraction = 4 # consider top third candidate keywords by score
def __init__(self): # self.stopwords = set(nltk.corpus.stopwords.words()) self.stopwords = getList() self.top_fraction = 2 # consider top third candidate keywords by score
def __init__(self, additional_stopwords):
#self.stopwords = set(nltk.corpus.stopwords.words())
self.stopwords = set(getList())
self.stopwords = self.stopwords | additional_stopwords
self.top_fraction = 3 # consider top third candidate keywords by score
def __init__(self):
# self.stopwords = set(nltk.corpus.stopwords.words())
self.stopwords = getList()
self.top_fraction = 1.5 # consider top third candidate keywords by score
import operator import sys from textblob import TextBlob from rake import RakeKeywordExtractor from textblob.np_extractors import ConllExtractor from nltk.stem import WordNetLemmatizer from nltk.tokenize import WordPunctTokenizer from textblob.taggers import NLTKTagger from stopwordList import getList import codecs ## GLOBAL VARIABLES top_fraction = 1 LEMMA_OBJ = WordNetLemmatizer() tokenizer = WordPunctTokenizer() nltk_tagger = NLTKTagger() stopwords = getList() COLL_OBJ = ConllExtractor() def rake_extract(phrase_list): RAKE_OBJ = RakeKeywordExtractor(set([])) word_scores = RAKE_OBJ._calculate_word_scores(phrase_list) phrase_scores = RAKE_OBJ._calculate_phrase_scores(phrase_list, word_scores) sorted_phrase_scores = sorted(phrase_scores.iteritems(),key=operator.itemgetter(1), reverse=True) n_phrases = len(sorted_phrase_scores) return sorted_phrase_scores[0:int(n_phrases)] #FILE = open(sys.argv[1],"r") FILE = codecs.open(sys.argv[1],"r","iso8859-15") CONTENT = FILE.read()