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6
7
8 """
9 Corpus reader for the Recognizing Textual Entailment (RTE) Challenge Corpora.
10
11 The files were taken from the RTE1, RTE2 and RTE3 datasets and the filenames
12 were regularized.
13
14 Filenames are of the form rte*_dev.xml and rte*_test.xml. The latter are the gold standard annotated files.
15
16 Each entailment corpus is a list of 'text'/'hypothesis' pairs. The following example is taken from RTE3::
17
18 <pair id="1" entailment="YES" task="IE" length="short" >
19 <t>The sale was made to pay Yukos' US$ 27.5 billion tax bill, Yuganskneftegaz was originally sold for US$ 9.4 billion to a little known company Baikalfinansgroup which was later bought by the Russian state-owned oil company Rosneft .</t>
20 <h>Baikalfinansgroup was sold to Rosneft.</h>
21 </pair>
22
23 In order to provide globally unique IDs for each pair, a new attribute C{challenge} has been added to the root element C{entailment-corpus} of each file, taking values 1, 2 or 3. The GID is formatted 'm-n', where 'm' is the challenge number and 'n' is the pair ID.
24 """
25
26 from util import *
27 from api import *
28 from xmldocs import XMLCorpusReader
29
30 -def norm(value_string):
31 """
32 Normalize the string value in an RTE pair's C{value} or C{entailment}
33 attribute as an integer (1, 0).
34
35 @param value_string: the label used to classify a text/hypothesis pair
36 @type value_string: C{str}
37 @rtype: C{int}
38 """
39
40 valdict = {"TRUE": 1,
41 "FALSE": 0,
42 "YES": 1,
43 "NO": 0}
44 return valdict[value_string.upper()]
45
47 """
48 Container for RTE text-hypothesis pairs.
49
50 The entailment relation is signalled by the C{value} attribute in RTE1, and by
51 C{entailment} in RTE2 and RTE3. These both get mapped on to the C{entailment}
52 attribute of this class.
53 """
54 - def __init__(self, pair, challenge=None, id=None, text=None, hyp=None,
55 value=None, task=None, length=None):
56 """
57 @param challenge: version of the RTE challenge (i.e., RTE1, RTE2 or RTE3)
58 @param id: identifier for the pair
59 @param text: the text component of the pair
60 @param hyp: the hypothesis component of the pair
61 @param value: classification label for the pair
62 @param task: attribute for the particular NLP task that the data was drawn from
63 @param length: attribute for the length of the text of the pair
64 """
65 self.challenge = challenge
66 self.id = pair.attrib["id"]
67 self.gid = "%s-%s" % (self.challenge, self.id)
68 self.text = pair[0].text
69 self.hyp = pair[1].text
70
71 if "value" in pair.attrib:
72 self.value = norm(pair.attrib["value"])
73 elif "entailment" in pair.attrib:
74 self.value = norm(pair.attrib["entailment"])
75 else:
76 self.value = value
77 if "task" in pair.attrib:
78 self.task = pair.attrib["task"]
79 else:
80 self.task = task
81 if "length" in pair.attrib:
82 self.length = pair.attrib["length"]
83 else:
84 self.length = length
85
87 if self.challenge:
88 return '<RTEPair: gid=%s-%s>' % (self.challenge, self.id)
89 else:
90 return '<RTEPair: id=%s>' % self.id
91
92
93
95 """
96 Corpus reader for corpora in RTE challenges.
97 """
99 """
100 Build a list of RTE text/hypothesis pairs from the XML input.
101
102 @param doc: a parsed XML document
103 @return: a list of C{RTEPair}s
104 """
105 try:
106 challenge = doc.attrib['challenge']
107 except KeyError:
108 challenge = None
109 return [RTEPair(pair, challenge=challenge)
110 for pair in doc.getiterator("pair")]
111
112
113 - def pairs(self, files=None):
123