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ILP_FeatureExtraction_old.py
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ILP_FeatureExtraction_old.py
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import fio
import json
import sys
import porter
import NLTKWrapper
import os
import numpy
import NumpyWrapper
import Survey
import ILP_baseline as ILP
import ILP_SVD
from collections import defaultdict
import util
from feat_vec import FeatureVector
from ILP_baseline import stopwords, stopwordswithpunctuations
import get_ngram_NP
import get_ngram_pos
import get_ngram_tfidf
nonwords = [line.lower().strip() for line in fio.ReadFile("../../data/wordsEn.txt")]
nonwords = [porter.getStemming(w) for w in nonwords]
phraseext = ".key" #a list
studentext = ".keys.source" #json
countext = ".dict" #a dictionary
featureext = ".f"
tfidfext = ".tfidf"
posext = '.pos'
inNPext = '.inNP'
titledir = "E:/Dropbox/reflection project_LRDC/250 Sp11 CLIC All Lecs .2G/titles/"
prompts = {'POI':'Describe what you found most interesting in today\'s class.',
'MP':'Describe what was confusing or needed more detail.',
'LP':'Describe what you learned about how you learn.'}
def getNgram(prefix, ngram):
phrases, bigrams, PhraseBigram = ILP.getPhraseBigram(prefix + phraseext, Ngram=ngram)
#get weight of bigrams {bigram:weigth}
BigramTheta = ILP.getBigramWeight_TF(PhraseBigram, phrases, prefix + countext) # return a dictionary
dict = {}
for bigram, tf in BigramTheta.items():
bigramname = bigrams[bigram]
dict[bigramname] = tf
return dict
def extract_TF(prefix, ngram):
phrases, bigrams, PhraseBigram = ILP.getPhraseBigram(prefix + phraseext, Ngram=ngram)
#get weight of bigrams {bigram:weigth}
BigramTheta = ILP.getBigramWeight_TF(PhraseBigram, phrases, prefix + countext) # return a dictionary
dict = {}
for bigram, tf in BigramTheta.items():
bigramname = bigrams[bigram]
feat_vec = FeatureVector()
if tf == 0: feat_vec['term_freq=0'] = 1.0 #remove
if tf >= 1: feat_vec['term_freq>=1'] = 1.0
if tf >= 2: feat_vec['term_freq>=2'] = 1.0
if tf >= 5: feat_vec['term_freq>=5'] = 1.0
if tf >= 10: feat_vec['term_freq>=10'] = 1.0 #remove
if tf != 0: feat_vec['term_freq!=0'] = 1.0 #remove
if tf < 1: feat_vec['term_freq<1'] = 1.0
if tf < 2: feat_vec['term_freq<2'] = 1.0
if tf < 5: feat_vec['term_freq<5'] = 1.0
if tf < 10: feat_vec['term_freq<10'] = 1.0
dict[bigramname] = feat_vec
return dict
def extract_TF_Rank(prefix, ngram, topK=10):
phrases, bigrams, PhraseBigram = ILP.getPhraseBigram(prefix + phraseext, Ngram=ngram)
#get weight of bigrams {bigram:weigth}
BigramTheta = ILP.getBigramWeight_TF(PhraseBigram, phrases, prefix + countext) # return a dictionary
keys = sorted(BigramTheta, key=BigramTheta.get, reverse=True)
dict = {}
for i, bigram in enumerate(keys):
bigramname = bigrams[bigram]
feat_vec = FeatureVector()
for k in range(i, topK):
feat_vec['tf_rank<=' + str(k)] = 1.0
minK = min(topK, i)
for k in range(0, minK):
feat_vec['tf_rank>' + str(k)] = 1.0
dict[bigramname] = feat_vec
return dict
def extract_TFIDF(prefix, ngram):
BigramTFIDF = fio.LoadDict(prefix + tfidfext, float)
dict = {}
for bigram, tfidf in BigramTFIDF.items():
feat_vec = FeatureVector()
if tfidf >= 0.5: feat_vec['tfidf>=0.5'] = 1.0
if tfidf >= 0.1: feat_vec['tfidf>=0.1'] = 1.0
if tfidf >= 0.05: feat_vec['tfidf>=0.05'] = 1.0
if tfidf >= 0.04: feat_vec['tfidf>=0.04'] = 1.0
if tfidf >= 0.03: feat_vec['tfidf>=0.03'] = 1.0
if tfidf >= 0.02: feat_vec['tfidf>=0.02'] = 1.0
if tfidf >= 0.01: feat_vec['tfidf>=0.01'] = 1.0
if tfidf >= 0.005: feat_vec['tfidf>=0.005'] = 1.0
if tfidf < 0.005: feat_vec['tfidf<0.005'] = 1.0
if tfidf < 0.5: feat_vec['tfidf<0.5'] = 1.0
if tfidf < 0.1: feat_vec['tfidf<0.1'] = 1.0
if tfidf < 0.05: feat_vec['tfidf<0.05'] = 1.0
if tfidf < 0.04: feat_vec['tfidf<0.04'] = 1.0
if tfidf < 0.03: feat_vec['tfidf<0.03'] = 1.0
if tfidf < 0.02: feat_vec['tfidf<0.02'] = 1.0
if tfidf < 0.01: feat_vec['tfidf<0.01'] = 1.0
if tfidf < 0.005: feat_vec['tfidf<0.005'] = 1.0
if tfidf >= 0.005: feat_vec['tfidf>0.005'] = 1.0
dict[bigram] = feat_vec
return dict
def extract_TFIDF_Rank(prefix, ngram, topK=10):
BigramTFIDF = fio.LoadDict(prefix + tfidfext, float)
keys = sorted(BigramTFIDF, key=BigramTFIDF.get, reverse=True)
dict = {}
for i, bigram in enumerate(keys):
feat_vec = FeatureVector()
for k in range(i, topK):
feat_vec['tfidf_rank<=' + str(k)] = 1.0
minK = min(topK, i)
for k in range(0, minK):
feat_vec['tfidf_rank>' + str(k)] = 1.0
dict[bigram] = feat_vec
return dict
def extract_Pos(prefix, ngram):
BigramPos = fio.LoadDict(prefix + posext, str)
dict = {}
for bigram, pos in BigramPos.items():
feat_vec = FeatureVector()
feat_vec['pos='+pos] = 1.0
if len(bigram.split()) == 2:
pos2 = pos.split(' ')
assert(len(pos2) == 2)
feat_vec['pos_first='+pos2[0]] = 1.0
feat_vec['pos_second='+pos2[1]] = 1.0
dict[bigram] = feat_vec
#TODO
return dict
def extract_inNP(prefix, ngram):
BigraminNP = fio.LoadDict(prefix + inNPext, str)
dict = {}
for bigram, inNP in BigraminNP.items():
feat_vec = FeatureVector()
if inNP == '1': feat_vec['in_NP_Yes'] = 1.0
if inNP == '0': feat_vec['in_NP_No'] = 1.0
dict[bigram] = feat_vec
return dict
def extract_averageSentenceLength(prefix, ngram):
IndexPhrase, IndexBigram, PhraseBigram = ILP.getPhraseBigram(prefix + phraseext, Ngram=ngram)
#get word count of phrases
PhraseBeta = ILP.getWordCounts(IndexPhrase)
BigramPhrase = ILP.getBigramPhrase(PhraseBigram)
dict = {}
for bigram, phrases in BigramPhrase.items():
bigramname = IndexBigram[bigram]
feat_vec = FeatureVector()
ave = 0
for p in phrases:
ave += PhraseBeta[p]
ave /= len(phrases)
if ave <= 2: feat_vec['ave_sen_length<=2'] = 1.0
if ave > 2 and ave <= 4: feat_vec['ave_sen_length=3-4'] = 1.0
if ave > 4 and ave <= 6: feat_vec['ave_sen_length=5-6'] = 1.0
if ave > 6 and ave <= 8: feat_vec['ave_sen_length=7-8'] = 1.0
if ave > 8 and ave <= 10: feat_vec['ave_sen_length=9-10'] = 1.0
if ave > 10 and ave <= 15: feat_vec['ave_sen_length=11-15'] = 1.0
if ave > 15 and ave <= 20: feat_vec['ave_sen_length=16-20'] = 1.0
if ave > 20 and ave <= 25: feat_vec['ave_sen_length=21-25'] = 1.0
if ave > 25 and ave <= 30: feat_vec['ave_sen_length=26-30'] = 1.0
if ave > 30: feat_vec['ave_sen_length>=30'] = 1.0
if ave > 2: feat_vec['ave_sen_length>2'] = 1.0
if ave <= 2 or ave > 4: feat_vec['ave_sen_length<=2,>4'] = 1.0
if ave <= 4 or ave > 6: feat_vec['ave_sen_length<=4,>6'] = 1.0
if ave <= 6 or ave > 8: feat_vec['ave_sen_length<=6,>8'] = 1.0
if ave <= 8 or ave > 10: feat_vec['ave_sen_length<=8,>10'] = 1.0
if ave <= 10 or ave > 15: feat_vec['ave_sen_length<=10,>15'] = 1.0
if ave <= 15 or ave > 20: feat_vec['ave_sen_length<=15,>20'] = 1.0
if ave <= 20 or ave > 25: feat_vec['ave_sen_length<=20,>25'] = 1.0
if ave <= 25 or ave > 30: feat_vec['ave_sen_length<=25,>30'] = 1.0
if ave <= 30: feat_vec['ave_sen_length<=30'] = 1.0
dict[bigramname] = feat_vec
return dict
def getType(prefix):
begin_p = prefix.rfind('/')
name = prefix[begin_p+1:]
end_p = name.find('.')
type = name[:end_p]
return type
def getLexiconOverlap(s1, s2):
tokens1 = s1.split()
tokens2 = s2.split()
unigram1 = ILP.getNgramTokenized(tokens1, 1, NoStopWords=True)
unigram2 = ILP.getNgramTokenized(tokens2, 1, NoStopWords=True)
overlap = 0
for w in unigram1:
if w in unigram2:
overlap += 1
return overlap
def extract_averageSentenceSimilarity2promp(prefix, ngram):
type = getType(prefix)
promp = prompts[type]
IndexPhrase, IndexBigram, PhraseBigram = ILP.getPhraseBigram(prefix + phraseext, Ngram=ngram)
#get word count of phrases
BigramPhrase = ILP.getBigramPhrase(PhraseBigram)
dict = {}
for bigram, phrases in BigramPhrase.items():
bigramname = IndexBigram[bigram]
feat_vec = FeatureVector()
ave = 0
for p in phrases:
ave += getLexiconOverlap(IndexPhrase[p], promp)
ave /= len(phrases)
if ave == 0: feat_vec['ave_sen_sim=0'] = 1.0
if ave <= 1: feat_vec['ave_sen_sim<=1'] = 1.0
if ave <= 2: feat_vec['ave_sen_sim<=2'] = 1.0
if ave <= 3: feat_vec['ave_sen_sim<=3'] = 1.0
if ave <= 4: feat_vec['ave_sen_sim<=4'] = 1.0
if ave <= 10: feat_vec['ave_sen_sim<=10'] = 1.0
if ave > 10: feat_vec['ave_sen_sim>10'] = 1.0
if ave != 0: feat_vec['ave_sen_sim!=0'] = 1.0
if ave > 1: feat_vec['ave_sen_sim>1'] = 1.0
if ave > 2: feat_vec['ave_sen_sim>2'] = 1.0
if ave > 3: feat_vec['ave_sen_sim>3'] = 1.0
if ave > 4: feat_vec['ave_sen_sim>4'] = 1.0
if ave > 10: feat_vec['ave_sen_sim>10'] = 1.0
dict[bigramname] = feat_vec
return dict
def extract_averageSentenceLength_rank(prefix, ngram, topK=10):
IndexPhrase, IndexBigram, PhraseBigram = ILP.getPhraseBigram(prefix + phraseext, Ngram=ngram)
#get word count of phrases
PhraseBeta = ILP.getWordCounts(IndexPhrase)
BigramPhrase = ILP.getBigramPhrase(PhraseBigram)
LengthDict = {}
dict = {}
for bigram, phrases in BigramPhrase.items():
bigramname = IndexBigram[bigram]
feat_vec = FeatureVector()
ave = 0
for p in phrases:
ave += PhraseBeta[p]
ave /= len(phrases)
LengthDict[bigramname] = ave
keys = sorted(LengthDict, key=LengthDict.get, reverse=True)
dict = {}
for i, bigramname in enumerate(keys):
feat_vec = FeatureVector()
for k in range(i, topK):
feat_vec['ave_sen_length_rank<=' + str(k)] = 1.0
minK = min(topK, i)
for k in range(0, minK):
feat_vec['ave_sen_length_rank>' + str(k)] = 1.0
dict[bigramname] = feat_vec
return dict
def extract_title(prefix, ngram, titlefile):
phrases, bigrams, PhraseBigram = ILP.getPhraseBigram(prefix + phraseext, Ngram=ngram)
titles = Survey.getTitle(titlefile)
titledict = {} #the ngram vocabulary in the titles
for title in titles:
#get stemming
phrase = porter.getStemming(title.lower())
tokens = phrase.lower().split()
#tokens = list(gensim.utils.tokenize(phrase, lower=True, errors='ignore'))
#get bigrams
ngrams = []
for n in ngram:
grams = ILP.getNgramTokenized(tokens, n, NoStopWords=True)
ngrams = ngrams + grams
for word in ngrams:
titledict[word.lower()] = True
dict = {}
for bigram in bigrams:
bigramname = bigrams[bigram]
feat_vec = FeatureVector()
if bigramname.lower() in titledict:
feat_vec['in_title_Yes'] = 1.0
else:
feat_vec['in_title_No'] = 1.0
dict[bigramname] = feat_vec
return dict
def extract_one(prefix, ngram):
phrases, bigrams, PhraseBigram = ILP.getPhraseBigram(prefix + phraseext, Ngram=ngram)
#get weight of bigrams {bigram:weigth}
BigramTheta = ILP.getBigramWeight_TF(PhraseBigram, phrases, prefix + countext) # return a dictionary
dict = {}
for bigram, tf in BigramTheta.items():
bigramname = bigrams[bigram]
dict[bigramname] = 1
return dict
def get_nonstop_ratio(bigram):
words = bigram.split()
stop_wc = 0.0
for word in words:
if word in stopwords:
stop_wc = stop_wc + 1.0
return 1-stop_wc/len(words)
def get_word_ratio(bigram):
words = bigram.split()
stop_wc = 0.0
for word in words:
if word in nonwords:
stop_wc = stop_wc + 1.0
return 1-stop_wc/len(words)
def extract_nonstop_ratio(prefix, ngram):
phrases, bigrams, PhraseBigram = ILP.getPhraseBigram(prefix + phraseext, Ngram=ngram)
dict = {}
for bigram in bigrams:
bigramname = bigrams[bigram]
feat_vec = FeatureVector()
if len(bigramname.split()) == 2:
words = bigramname.split()
if words[0] in stopwordswithpunctuations:
feat_vec['stop_first=True'] = 1.0
if words[1] in stopwordswithpunctuations:
feat_vec['stop_second=True'] = 1.0
# r = get_nonstop_ratio(bigramname)
# if r <= 0.5: feat_vec['non_stop_ratio<=0.5'] = 1.0
# if r > 0.5: feat_vec['non_stop_ratio>0.5'] = 1.0
dict[bigramname] = feat_vec
return dict
def extract_word_ratio(prefix, ngram):
phrases, bigrams, PhraseBigram = ILP.getPhraseBigram(prefix + phraseext, Ngram=ngram)
dict = {}
for bigram in bigrams:
bigramname = bigrams[bigram]
feat_vec = FeatureVector()
r = get_word_ratio(bigramname)
if r <= 0.5: feat_vec['word_ratio<=0.5'] = 1.0
if r > 0.5: feat_vec['word_ratio>0.5'] = 1.0
dict[bigramname] = feat_vec
return dict
def get_word_count_dis(input, CountFile):
lines = fio.ReadFile(input)
phrases = [line.strip() for line in lines]
CountDict = fio.LoadDict(CountFile, 'float')
dict = defaultdict(float)
for phrase in phrases:
fequency = CountDict[phrase]
#get stemming
phrase = porter.getStemming(phrase)
tokens = phrase.lower().split()
words = ILP.getNgramTokenized(tokens, 1, NoStopWords=True)
for word in words:
dict[word] += fequency
return dict
def extract_frequency_of_words(prefix, ngram):
dict = {}
#get all the word frequency
wf_dict = get_word_count_dis(prefix + phraseext, prefix + countext)
phrases, bigrams, PhraseBigram = ILP.getPhraseBigram(prefix + phraseext, Ngram=ngram)
for bigram in bigrams:
if bigram == 'all':
print "all"
bigramname = bigrams[bigram]
feat_vec = FeatureVector()
words = bigramname.split()
if len(words) == 2:
n1 = wf_dict[words[0]]
n2 = wf_dict[words[1]]
if n1 >= 1: feat_vec['freq_firstw>=1'] = 1.0
if n1 >= 2: feat_vec['freq_firstw>=2'] = 1.0
if n1 >= 3: feat_vec['freq_firstw>=3'] = 1.0
if n1 >= 4: feat_vec['freq_firstw>=4'] = 1.0
if n1 >= 5: feat_vec['freq_firstw>=5'] = 1.0
if n1 >= 10: feat_vec['freq_firstw>=10'] = 1.0
if n2 >= 1: feat_vec['freq_secondw>=1'] = 1.0
if n2 >= 2: feat_vec['freq_secondw>=2'] = 1.0
if n2 >= 3: feat_vec['freq_secondw>=3'] = 1.0
if n2 >= 4: feat_vec['freq_secondw>=4'] = 1.0
if n2 >= 5: feat_vec['freq_secondw>=5'] = 1.0
if n2 >= 10: feat_vec['freq_secondw>=10'] = 1.0
if n1 < 1: feat_vec['freq_firstw<1'] = 1.0
if n1 < 2: feat_vec['freq_firstw<2'] = 1.0
if n1 < 3: feat_vec['freq_firstw<3'] = 1.0
if n1 < 4: feat_vec['freq_firstw<4'] = 1.0
if n1 < 5: feat_vec['freq_firstw<5'] = 1.0
if n1 < 10: feat_vec['freq_firstw<10'] = 1.0
if n2 < 1: feat_vec['freq_secondw<1'] = 1.0
if n2 < 2: feat_vec['freq_secondw<2'] = 1.0
if n2 < 3: feat_vec['freq_secondw<3'] = 1.0
if n2 < 4: feat_vec['freq_secondw<4'] = 1.0
if n2 < 5: feat_vec['freq_secondw<5'] = 1.0
if n2 < 10: feat_vec['freq_secondw<10'] = 1.0
else:#unigram
n1 = wf_dict[words[0]]
if n1 >= 1: feat_vec['freq_firstw>=1'] = 1.0
if n1 >= 2: feat_vec['freq_firstw>=2'] = 1.0
if n1 >= 3: feat_vec['freq_firstw>=3'] = 1.0
if n1 >= 4: feat_vec['freq_firstw>=4'] = 1.0
if n1 >= 5: feat_vec['freq_firstw>=5'] = 1.0
if n1 >= 10: feat_vec['freq_firstw>=10'] = 1.0
if n1 < 1: feat_vec['freq_firstw<1'] = 1.0
if n1 < 2: feat_vec['freq_firstw<2'] = 1.0
if n1 < 3: feat_vec['freq_firstw<3'] = 1.0
if n1 < 4: feat_vec['freq_firstw<4'] = 1.0
if n1 < 5: feat_vec['freq_firstw<5'] = 1.0
if n1 < 10: feat_vec['freq_firstw<10'] = 1.0
dict[bigramname] = feat_vec
return dict
def extract_ngram_length(prefix, ngram):
phrases, bigrams, PhraseBigram = ILP.getPhraseBigram(prefix + phraseext, Ngram=ngram)
dict = {}
for bigram in bigrams:
bigramname = bigrams[bigram]
feat_vec = FeatureVector()
if len(bigramname.split()) == 1: feat_vec['ngram=1'] = 1.0
if len(bigramname.split()) == 2: feat_vec['ngram=2'] = 1.0
dict[bigramname] = feat_vec
return dict
def getPosition(bigram, phrase, bin=5):
tokens = phrase.lower().split()
if len(bigram.split()) == 2:
bigrams = ILP.getNgramTokenized(tokens, 2, NoStopWords=False, Stemmed=True)
else:
bigrams = ILP.getNgramTokenized(tokens, 1, NoStopWords=False, Stemmed=True)
N = len(bigrams)
index = bigrams.index(bigram)
assert(index != -1)
return util.get_bin_index(index, 0, N, bin)
def extract_position(prefix, ngram, bin):
IndexPhrase, IndexBigram, PhraseBigram = ILP.getPhraseBigram(prefix + phraseext, Ngram=ngram)
BigramPhrase = ILP.getBigramPhrase(PhraseBigram)
dict = {}
for bigram, phrases in BigramPhrase.items():
bigramname = IndexBigram[bigram]
feat_vec = FeatureVector()
for phrase in phrases: #union of the position feature
p = getPosition(bigramname, IndexPhrase[phrase])
feat_vec['position_'+str(p)] = 1.0
dict[bigramname] = feat_vec
return dict
def add_feature_set(todict, fromdict):
for k, v in fromdict.items():
if k not in todict:
todict[k] = {}
if type(v) == dict or type(v) == FeatureVector:
for name, val in v.iteritems():
todict[k][name] = val
return todict
def add_bias(fromdict):
for k, v in fromdict.items():
if type(v) == dict or type(v) == FeatureVector:
fromdict[k]['b'] = 1.0
return fromdict
def extract_single(prefix, ngram, output, titlefile=None, features = None, position_bin = 5):
data = {}
tf_dict = extract_TF(prefix, ngram)
tf_rank_dict = extract_TF_Rank(prefix, ngram)
idftf_dict = extract_TFIDF(prefix, ngram)
idftf_rank_dict = extract_TFIDF_Rank(prefix, ngram)
pos_dict = extract_Pos(prefix, ngram)
inNP_dict = extract_inNP(prefix, ngram)
ave_length_dict = extract_averageSentenceLength(prefix, ngram)
ave_length_dict_rank = extract_averageSentenceLength_rank(prefix, ngram)
ave_sim_dict = extract_averageSentenceSimilarity2promp(prefix, ngram)
one_dict = extract_one(prefix, ngram)
stop_ratio_dict = extract_nonstop_ratio(prefix, ngram)
word_ratio_dict = extract_word_ratio(prefix, ngram)
ngram_length_dict = extract_ngram_length(prefix, ngram)
frequency_of_words_dict = extract_frequency_of_words(prefix, ngram)
if titlefile != None:
title_dict = extract_title(prefix, ngram, titlefile)
else:
title_dict = {}
position_dict = extract_position(prefix, ngram, bin=position_bin)
if 'tf' in features:
data = add_feature_set(data, tf_dict)
if 'tf_rank' in features:
data = add_feature_set(data, tf_rank_dict)
if 'idftf' in features:
data = add_feature_set(data, idftf_dict)
if 'idftf_rank' in features:
data = add_feature_set(data, idftf_rank_dict)
if 'pos' in features:
data = add_feature_set(data, pos_dict)
if 'inNP' in features:
data = add_feature_set(data, inNP_dict)
if 'ave_length' in features:
data = add_feature_set(data, ave_length_dict)
if 'ave_length_rank' in features:
data = add_feature_set(data, ave_length_dict_rank)
if 'ave_sim' in features:
data = add_feature_set(data, ave_sim_dict)
if 'stop_word' in features:
data = add_feature_set(data, stop_ratio_dict)
if 'word_ratio' in features:
data = add_feature_set(data, word_ratio_dict)
if 'ngram_length' in features:
data = add_feature_set(data, ngram_length_dict)
if 'frequency_of_words' in features:
data = add_feature_set(data, frequency_of_words_dict)
if 'title' in features:
data = add_feature_set(data, title_dict)
if 'position' in features:
data = add_feature_set(data, position_dict)
#data = add_bias(data)
with open(output, 'w') as outfile:
json.dump(data, outfile, indent=2)
def extact(ilpdir, np, ngram, features, position_bin):
sheets = range(0,12)
for i, sheet in enumerate(sheets):
week = i + 1
dir = ilpdir + str(week) + '/'
for type in ['POI', 'MP', 'LP']:
prefix = dir + type
feature_file = prefix + featureext
prefix = prefix + '.' + np
print feature_file
titlefile = titledir + str(week) + '.TXT'
extract_single(prefix, ngram, feature_file, titlefile, features, position_bin)
if __name__ == '__main__':
from config import ConfigFile
config = ConfigFile()
sennadatadir = "../../data/senna/"
for ilpdir in ["../../data/ILP_Sentence_Supervised_FeatureWeighting/",
"../../data/ILP_Sentence_Supervised_FeatureWeightingAveragePerceptron/",
#"../../data/ILP_Sentence_Supervised_FeatureWeightingMC/"
]:
get_ngram_NP.extact_inNP(ilpdir, sennadatadir, np = 'sentence', ngram=config.get_ngrams())
get_ngram_tfidf.extact_tfidf(ilpdir, np = 'sentence', ngram=config.get_ngrams())
get_ngram_pos.extact_pos(ilpdir, sennadatadir, np = 'sentence', ngram=config.get_ngrams())
extact(ilpdir, np = 'sentence', ngram=config.get_ngrams(), features=config.get_features(), position_bin = config.get_position_bin())