def performTask(rawtext):
text=textprocess.preprocess(rawtext)
POS_text = pt.tag(text)
print POS_text
#Didn't do toLower earlier because the stanford tagger might make
# use of the capitalizations
text = text.lower()
skilldict = buildskilldict(skills)
naive_skills= generate_naive(text,skilldict)
expanded_skills = new_guesses(POS_text,naive_skills,words,clusters)
return naive_skills, expanded_skills
def crackr(text, skillfilter=None):
# skillfilter can be one of
# - None: don't filter
# - "pre": filter a priori
# - "post": filter a posteriori
# preprocess text
text = textprocess.preprocess(text).lower()
# tokenize
sentenceList = splitSentences(text)
phraseList = generateCandidateKeywords(sentenceList, stopwordpattern)
wordscores = calculateWordScores(phraseList)
# generate ngrams
tokens = text.split()
# pre-filter
if skillfilter == 'pre':
tokens = [token for token in tokens if token in skilldict]
ngrams = [ngram for n in range(3) for ngram in generate_ngrams(tokens, n + 1)]
# filter clusters
viableclusters = []
for ngram in ngrams:
try:
viableclusters += [words[ngram] [1]]
except:
pass
# filter words by clusters
viablewords = []
for cluster in set(viableclusters):
for (word, _) in clusters[cluster]:
viablewords += [word]
viablewords = set(viablewords)
# pos tag
pos_tagged = pt.tag(text)
# filter single words
index = 0
finallst = []
indices = []
thirdlst = []
for tup in pos_tagged:
word, tag = tup
if tag == 'CC':
print word
print pos_tagged[index - 1] [1] [0], pos_tagged[index - 1] [1] [0]
if tag[0] == 'N' or tag[0] == 'J' or (tag == 'CC' and pos_tagged[index - 1] [1] [0] == 'N'):
if word.lower() in viablewords:
finallst += [(word)]
indices += [index]
thirdlst += [(word, index)]
index += 1
# generate keyword phrases
ngrams = stich(finallst, indices)
ngrams = [" ".join(ngram) for ngram in ngrams]
keywordcandidates = generateCandidateKeywordScores(phraseList, wordscores)
scored_ngrams = sorted(keywordcandidates.iteritems(), key=operator.itemgetter(1), reverse=True)
# post-filter
if skillfilter == 'post':
scored_ngrams = [(ngram, score) for (ngram, score) in scored_ngrams if ngram in skilldict]
# format
return [{'keyword':pair[0], 'weight':pair[1]} for pair in scored_ngrams]
