Code Coverage for nltk.corpus.reader.rte
Partially Tested Functions
"""
Corpus reader for the Recognizing Textual Entailment (RTE) Challenge Corpora.
The files were taken from the RTE1, RTE2 and RTE3 datasets and the filenames
were regularized.
Filenames are of the form rte*_dev.xml and rte*_test.xml. The latter are the gold standard annotated files.
Each entailment corpus is a list of 'text'/'hypothesis' pairs. The following example is taken from RTE3::
<pair id="1" entailment="YES" task="IE" length="short" >
<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>
<h>Baikalfinansgroup was sold to Rosneft.</h>
</pair>
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.
"""
from util import *
from api import *
from xmldocs import XMLCorpusReader
def norm(value_string):
"""
Normalize the string value in an RTE pair's C{value} or C{entailment}
attribute as an integer (1, 0).
@param value_string: the label used to classify a text/hypothesis pair
@type value_string: C{str}
@rtype: C{int}
"""
valdict = {"TRUE": 1,
"FALSE": 0,
"YES": 1,
"NO": 0}
return valdict[value_string.upper()]
class RTEPair:
"""
Container for RTE text-hypothesis pairs.
The entailment relation is signalled by the C{value} attribute in RTE1, and by
C{entailment} in RTE2 and RTE3. These both get mapped on to the C{entailment}
attribute of this class.
"""
def __init__(self, pair, challenge=None, id=None, text=None, hyp=None,
value=None, task=None, length=None):
"""
@param challenge: version of the RTE challenge (i.e., RTE1, RTE2 or RTE3)
@param id: identifier for the pair
@param text: the text component of the pair
@param hyp: the hypothesis component of the pair
@param value: classification label for the pair
@param task: attribute for the particular NLP task that the data was drawn from
@param length: attribute for the length of the text of the pair
"""
self.challenge = challenge
self.id = pair.attrib["id"]
self.gid = "%s-%s" % (self.challenge, self.id)
self.text = pair[0].text
self.hyp = pair[1].text
if "value" in pair.attrib:
self.value = norm(pair.attrib["value"])
elif "entailment" in pair.attrib:
self.value = norm(pair.attrib["entailment"])
else:
self.value = value
if "task" in pair.attrib:
self.task = pair.attrib["task"]
else:
self.task = task
if "length" in pair.attrib:
self.length = pair.attrib["length"]
else:
self.length = length
def __repr__(self):
if self.challenge:
return '<RTEPair: gid=%s-%s>' % (self.challenge, self.id)
else:
return '<RTEPair: id=%s>' % self.id
class RTECorpusReader(XMLCorpusReader):
"""
Corpus reader for corpora in RTE challenges.
"""
def _read_etree(self, doc):
"""
Build a list of RTE text/hypothesis pairs from the XML input.
@param doc: a parsed XML document
@return: a list of C{RTEPair}s
"""
try:
challenge = doc.attrib['challenge']
except KeyError:
challenge = None
return [RTEPair(pair, challenge=challenge)
for pair in doc.getiterator("pair")]
def pairs(self, files=None):
"""
Build a list of RTE pairs from a RTE corpus.
"""
doc = self.xml(files)
if doc.tag == 'documents':
return concat([self._read_etree(corpus)
for corpus in doc.getchildren()])
else:
return self._read_etree(doc)