import pickle
from sklearn.feature_extraction import DictVectorizer
from itertools import chain
import nltk
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.preprocessing import LabelBinarizer
import sklearn
import pycrfsuite
from loadTuples import load, load2, load3
from sklearn import svm
from evalt import *
from collections import Counter
import sentlex
test_sents = load3("test")
#print train_sents
#print "sent =" +str(len(train_sents))
SWN = sentlex.SWN3Lexicon()
f = open("PredictedTags.pkl", 'rb')
Eventpredicted = pickle.load(f)
f.close()
global wordCnt
wordCnt = -1
def word2features(sent, i):
"""get the feautes corresponding to a word in a sentence at a particular position
Args:
from sklearn.feature_extraction import DictVectorizer
from itertools import chain
import nltk
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.preprocessing import LabelBinarizer
import sklearn
import pycrfsuite
from loadTuples import load, load2, load3
from sklearn import svm
from evalt3 import *
import pickle
import sentlex
train_sents = load3("train")
# test_sents = load2("test")[:100]
#print train_sents
#print "sent =" +str(len(train_sents))
SWN = sentlex.SWN3Lexicon()
def word2features(sent, i):
"""get the feautes corresponding to a word in a sentence at a particular position
Args:
sent: the sentence whose word is to be considered
i: the position of the word in the sentence
Returns:
the dictionary containing the features for the classifier
"""
word = sent[i][0]
postag = sent[i][1]
norm = sent[i][2]
cui = sent[i][3]
import pickle
from sklearn.feature_extraction import DictVectorizer
from itertools import chain
import nltk
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.preprocessing import LabelBinarizer
import sklearn
import pycrfsuite
from loadTuples import load,load2, load3
from sklearn import svm
from evalt import *
import sentlex
from collections import Counter
test_sents = load3("test")
#print train_sents
#print "sent =" +str(len(train_sents))
SWN = sentlex.SWN3Lexicon()
f=open("PredictedTags.pkl", 'rb')
Eventpredicted = pickle.load(f)
f.close()
global wordCnt
wordCnt = -1
def word2features(sent, i):
"""get the feautes corresponding to a word in a sentence at a particular position
Args:
sent: the sentence whose word is to be considered
from sklearn.feature_extraction import DictVectorizer
from itertools import chain
import nltk
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.preprocessing import LabelBinarizer
import sklearn
import pycrfsuite
from loadTuples import load,load2,load3
from sklearn import svm
from evalt3 import *
import pickle
import sentlex
train_sents = load3("train")
# test_sents = load2("test")[:100]
#print train_sents
#print "sent =" +str(len(train_sents))
SWN = sentlex.SWN3Lexicon()
def word2features(sent, i):
"""get the feautes corresponding to a word in a sentence at a particular position
Args:
sent: the sentence whose word is to be considered
i: the position of the word in the sentence
Returns:
the dictionary containing the features for the classifier
"""
word = sent[i][0]
postag = sent[i][1]
norm = sent[i][2]
cui = sent[i][3]
tui = sent[i][4]