-
Notifications
You must be signed in to change notification settings - Fork 0
/
FeatureExtractor.py
81 lines (65 loc) · 2.61 KB
/
FeatureExtractor.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
from xml.etree import ElementTree as ET
from nltk import MaxentClassifier
from nltk import classify
TEST_CORP = "train_corp_tesing.xml"
PARAGRAPH = "_paragraph_"
isdigit = lambda t: t.isdigit()
title = lambda t: t.istitle()
def read(x):
with open(x, encoding='utf-8') as infile:
tree = ET.parse(infile)
return tree
# работа происходит параграфами, предложений "не существует".
def collect_classified_data(xml):
labeled_data = []
tree = read(xml)
paragraphs = tree.findall('.//parag')
for paragraph_node in paragraphs:
token_nodes = paragraph_node.findall('.//t')
ld = extract_labeled_data(token_nodes)
labeled_data += ld
return labeled_data
# на вход: ['_paragraph_' ... '.' ... '_paragraph_']
def extract_labeled_data(token_nodes):
labeled_data = []
for i in range(1,len(token_nodes)-1):
label = token_nodes[i].get('class')
if label:
prev = token_nodes[i-1].text
this = token_nodes[i].text
nxt = token_nodes[i+1].text
features = extract_features(prev,nxt)
labeled_data.append((features,label))
return labeled_data
# на вход два крайних токена: [prev '.' nxt]
# крайний вариант: [prev '.' '_paragraph_']
# или: ['_paragraph_' '.' nxt]
def extract_features(prev,nxt):
features = {}
if prev != PARAGRAPH:
features['left_neighbor_len'] = "%s" % len(prev)
features['left_neighbor_digit'] = "%s" % isdigit(prev)
features['left_neighbor_title'] = "%s" % title(prev)
else:
features['left_neighbor_len'] = "0"
features['left_neighbor_digit'] = "False"
features['left_neighbor_title'] = "False"
if nxt != PARAGRAPH:
features['right_neighbor_len'] = "%s" % len(nxt)
features['right_neighbor_digit'] = "%s" % isdigit(nxt)
features['right_neighbor_title'] = "%s" % title(nxt)
features['paragraph_end'] = "False"
else:
features['right_neighbor_len'] = "0"
features['right_neighbor_digit'] = "False"
features['right_neighbor_title'] = "False"
features['paragraph_end'] = "True"
return features
if __name__ == "__main__":
tree = "try_train.xml"
data = collect_classified_data(tree)
train_set, test_set = data, data
me_classifier = MaxentClassifier.train(train_set)
test_ex = test_set[0][0]
print(test_ex)
print(me_classifier.classify(test_ex))