def __init__(self):
self.tagger = load('taggers/maxent_treebank_pos_tagger/english.pickle')
self.reverb = Reverb()
self.lmtzr = WordNetLemmatizer()
self.aux_verbs = ['be', 'have']
def __init__(self, config_file, seeds_file, negative_seeds, sentences_file,
similarity, confidance):
self.seed_tuples = set()
self.negative_seed_tuples = set()
self.e1_type = None
self.e2_type = None
self.stopwords = stopwords.words('english')
self.threshold_similarity = similarity
self.instance_confidance = confidance
self.reverb = Reverb()
for line in fileinput.input(config_file):
if line.startswith("#") or len(line) == 1:
continue
if line.startswith("wUpdt"):
self.wUpdt = float(line.split("=")[1])
if line.startswith("wUnk"):
self.wUnk = float(line.split("=")[1])
if line.startswith("wNeg"):
self.wNeg = float(line.split("=")[1])
if line.startswith("number_iterations"):
self.number_iterations = int(line.split("=")[1])
if line.startswith("use_RlogF"):
self.use_RlogF = bool(line.split("=")[1])
if line.startswith("min_pattern_support"):
self.min_pattern_support = int(line.split("=")[1])
if line.startswith("max_tokens_away"):
self.max_tokens_away = int(line.split("=")[1])
if line.startswith("min_tokens_away"):
self.min_tokens_away = int(line.split("=")[1])
if line.startswith("context_window_size"):
self.context_window_size = int(line.split("=")[1])
if line.startswith("use_reverb"):
self.use_reverb = line.split("=")[1].strip()
if line.startswith("alpha"):
self.alpha = float(line.split("=")[1])
if line.startswith("beta"):
self.beta = float(line.split("=")[1])
if line.startswith("gamma"):
self.gamma = float(line.split("=")[1])
assert self.alpha + self.beta + self.gamma == 1
self.read_seeds(seeds_file)
self.read_negative_seeds(negative_seeds)
fileinput.close()
print "\nConfiguration parameters"
print "========================"
print "Relationship Representation"
print "e1 type :", self.e1_type
print "e2 type :", self.e2_type
print "context window :", self.context_window_size
print "max tokens away :", self.max_tokens_away
print "min tokens away :", self.min_tokens_away
print "use ReVerb :", self.use_reverb
print "\nVectors"
print "alpha :", self.alpha
print "beta :", self.beta
print "gamma :", self.gamma
print "\nSeeds:"
print "positive seeds :", len(self.seed_tuples)
print "negative seeds :", len(self.negative_seed_tuples)
print "negative seeds wNeg :", self.wNeg
print "unknown seeds wUnk :", self.wUnk
print "\nParameters and Thresholds"
print "threshold_similarity :", self.threshold_similarity
print "instance confidence :", self.instance_confidance
print "min_pattern_support :", self.min_pattern_support
print "iterations :", self.number_iterations
print "iteration wUpdt :", self.wUpdt
print "\n"
try:
os.path.isfile("vsm.pkl")
f = open("vsm.pkl", "r")
print "\nLoading tf-idf model from disk..."
self.vsm = cPickle.load(f)
f.close()
except IOError:
print "\nGenerating tf-idf model from sentences..."
self.vsm = VectorSpaceModel(sentences_file, self.stopwords)
print "\nWriting generated model to disk..."
f = open("vsm.pkl", "wb")
cPickle.dump(self.vsm, f)
f.close()
