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from __future__ import division
import string
from nltk.translate.bleu_score import sentence_bleu
from nltk.corpus import stopwords
from copy import copy
import ipdb
class Matcher:
@staticmethod
def bowMatch(ref, ex, ignoreStopwords, ignoreCase):
"""
A binary function testing for exact lexical match (ignoring ordering) between reference
and predicted extraction
"""
s1 = ref.bow()
s2 = ex.bow()
if ignoreCase:
s1 = s1.lower()
s2 = s2.lower()
s1Words = s1.split(' ')
s2Words = s2.split(' ')
if ignoreStopwords:
s1Words = Matcher.removeStopwords(s1Words)
s2Words = Matcher.removeStopwords(s2Words)
return sorted(s1Words) == sorted(s2Words)
@staticmethod
def predMatch(ref, ex, ignoreStopwords, ignoreCase):
"""
Return whehter gold and predicted extractions agree on the predicate
"""
s1 = ref.elementToStr(ref.pred)
s2 = ex.elementToStr(ex.pred)
if ignoreCase:
s1 = s1.lower()
s2 = s2.lower()
s1Words = s1.split(' ')
s2Words = s2.split(' ')
if ignoreStopwords:
s1Words = Matcher.removeStopwords(s1Words)
s2Words = Matcher.removeStopwords(s2Words)
return s1Words == s2Words
@staticmethod
def argMatch(ref, ex, ignoreStopwords, ignoreCase):
"""
Return whehter gold and predicted extractions agree on the arguments
"""
sRef = ' '.join([ref.elementToStr(elem) for elem in ref.args])
sEx = ' '.join([ex.elementToStr(elem) for elem in ex.args])
count = 0
for w1 in sRef:
for w2 in sEx:
if w1 == w2:
count += 1
# We check how well does the extraction lexically cover the reference
# Note: this is somewhat lenient as it doesn't penalize the extraction for
# being too long
coverage = float(count) / len(sRef)
return coverage > Matcher.LEXICAL_THRESHOLD
@staticmethod
def bleuMatch(ref, ex, ignoreStopwords, ignoreCase):
sRef = ref.bow()
sEx = ex.bow()
bleu = sentence_bleu(references = [sRef.split(' ')], hypothesis = sEx.split(' '))
return bleu > Matcher.BLEU_THRESHOLD
@staticmethod
def lexicalMatch(ref, ex, ignoreStopwords, ignoreCase):
sRef = ref.bow().split(' ')
sEx = ex.bow().split(' ')
count = 0
#for w1 in sRef:
# if w1 in sEx:
# count += 1
# sEx.remove(w1)
for w1 in sRef:
for w2 in sEx:
if w1 == w2:
count += 1
# We check how well does the extraction lexically cover the reference
# Note: this is somewhat lenient as it doesn't penalize the extraction for
# being too long
coverage = float(count) / len(sRef)
return coverage > Matcher.LEXICAL_THRESHOLD
@staticmethod
def tuple_match(ref, ex, ignoreStopwords, ignoreCase):
precision = [0, 0] # 0 out of 0 predicted words match
recall = [0, 0] # 0 out of 0 reference words match
# If, for each part, any word is the same as a reference word, then it's a match.
predicted_words = ex.pred.split()
gold_words = ref.pred.split()
precision[1] += len(predicted_words)
recall[1] += len(gold_words)
# matching_words = sum(1 for w in predicted_words if w in gold_words)
matching_words = 0
for w in gold_words:
if w in predicted_words:
matching_words += 1
predicted_words.remove(w)
if matching_words == 0:
return False # t <-> gt is not a match
precision[0] += matching_words
recall[0] += matching_words
for i in range(len(ref.args)):
gold_words = ref.args[i].split()
recall[1] += len(gold_words)
if len(ex.args) <= i:
if i<2:
return False
else:
continue
predicted_words = ex.args[i].split()
precision[1] += len(predicted_words)
matching_words = 0
for w in gold_words:
if w in predicted_words:
matching_words += 1
predicted_words.remove(w)
if matching_words == 0 and i<2:
return False # t <-> gt is not a match
precision[0] += matching_words
# Currently this slightly penalises systems when the reference
# reformulates the sentence words, because the reformulation doesn't
# match the predicted word. It's a one-wrong-word penalty to precision,
# to all systems that correctly extracted the reformulated word.
recall[0] += matching_words
prec = 1.0 * precision[0] / precision[1]
rec = 1.0 * recall[0] / recall[1]
return [prec, rec]
# STRICTER LINIENT MATCH
def linient_tuple_match(ref, ex, ignoreStopwords, ignoreCase):
precision = [0, 0] # 0 out of 0 predicted words match
recall = [0, 0] # 0 out of 0 reference words match
# If, for each part, any word is the same as a reference word, then it's a match.
predicted_words = ex.pred.split()
gold_words = ref.pred.split()
precision[1] += len(predicted_words)
recall[1] += len(gold_words)
# matching_words = sum(1 for w in predicted_words if w in gold_words)
matching_words = 0
for w in gold_words:
if w in predicted_words:
matching_words += 1
predicted_words.remove(w)
# matching 'be' with its different forms
forms_of_be = ["be","is","am","are","was","were","been","being"]
if "be" in predicted_words:
for form in forms_of_be:
if form in gold_words:
matching_words += 1
predicted_words.remove("be")
break
if matching_words == 0:
return [0,0] # t <-> gt is not a match
precision[0] += matching_words
recall[0] += matching_words
for i in range(len(ref.args)):
gold_words = ref.args[i].split()
recall[1] += len(gold_words)
if len(ex.args) <= i:
if i<2:
return [0,0] # changed
else:
continue
predicted_words = ex.args[i].split()
precision[1] += len(predicted_words)
matching_words = 0
for w in gold_words:
if w in predicted_words:
matching_words += 1
predicted_words.remove(w)
precision[0] += matching_words
# Currently this slightly penalises systems when the reference
# reformulates the sentence words, because the reformulation doesn't
# match the predicted word. It's a one-wrong-word penalty to precision,
# to all systems that correctly extracted the reformulated word.
recall[0] += matching_words
if(precision[1] == 0):
prec = 0
else:
prec = 1.0 * precision[0] / precision[1]
if(recall[1] == 0):
rec = 0
else:
rec = 1.0 * recall[0] / recall[1]
return [prec, rec]
@staticmethod
def simple_tuple_match(ref, ex, ignoreStopwords, ignoreCase):
ref.args = [ref.args[0], ' '.join(ref.args[1:])]
ex.args = [ex.args[0], ' '.join(ex.args[1:])]
precision = [0, 0] # 0 out of 0 predicted words match
recall = [0, 0] # 0 out of 0 reference words match
# If, for each part, any word is the same as a reference word, then it's a match.
predicted_words = ex.pred.split()
gold_words = ref.pred.split()
precision[1] += len(predicted_words)
recall[1] += len(gold_words)
matching_words = 0
for w in gold_words:
if w in predicted_words:
matching_words += 1
predicted_words.remove(w)
precision[0] += matching_words
recall[0] += matching_words
for i in range(len(ref.args)):
gold_words = ref.args[i].split()
recall[1] += len(gold_words)
if len(ex.args) <= i:
break
predicted_words = ex.args[i].split()
precision[1] += len(predicted_words)
matching_words = 0
for w in gold_words:
if w in predicted_words:
matching_words += 1
predicted_words.remove(w)
precision[0] += matching_words
# Currently this slightly penalises systems when the reference
# reformulates the sentence words, because the reformulation doesn't
# match the predicted word. It's a one-wrong-word penalty to precision,
# to all systems that correctly extracted the reformulated word.
recall[0] += matching_words
prec = 1.0 * precision[0] / precision[1]
rec = 1.0 * recall[0] / recall[1]
return [prec, rec]
# @staticmethod
# def binary_linient_tuple_match(ref, ex, ignoreStopwords, ignoreCase):
# if len(ref.args)>=2:
# # r = ref.copy()
# r = copy(ref)
# r.args = [ref.args[0], ' '.join(ref.args[1:])]
# else:
# r = ref
# if len(ex.args)>=2:
# # e = ex.copy()
# e = copy(ex)
# e.args = [ex.args[0], ' '.join(ex.args[1:])]
# else:
# e = ex
# return Matcher.linient_tuple_match(r, e, ignoreStopwords, ignoreCase)
@staticmethod
def binary_linient_tuple_match(ref, ex, ignoreStopwords, ignoreCase):
if len(ref.args)>=2:
r = copy(ref)
r.args = [ref.args[0], ' '.join(ref.args[1:])]
else:
r = ref
if len(ex.args)>=2:
e = copy(ex)
e.args = [ex.args[0], ' '.join(ex.args[1:])]
else:
e = ex
stright_match = Matcher.linient_tuple_match(r, e, ignoreStopwords, ignoreCase)
said_type_reln = ["said", "told", "added", "adds", "says", "adds"]
said_type_sentence = False
for said_verb in said_type_reln:
if said_verb in ref.pred:
said_type_sentence = True
break
if not said_type_sentence:
return stright_match
else:
if len(ex.args)>=2:
e = copy(ex)
e.args = [' '.join(ex.args[1:]), ex.args[0]]
else:
e = ex
reverse_match = Matcher.linient_tuple_match(r, e, ignoreStopwords, ignoreCase)
return max(stright_match, reverse_match)
@staticmethod
def binary_tuple_match(ref, ex, ignoreStopwords, ignoreCase):
if len(ref.args)>=2:
# r = ref.copy()
r = copy(ref)
r.args = [ref.args[0], ' '.join(ref.args[1:])]
else:
r = ref
if len(ex.args)>=2:
# e = ex.copy()
e = copy(ex)
e.args = [ex.args[0], ' '.join(ex.args[1:])]
else:
e = ex
return Matcher.tuple_match(r, e, ignoreStopwords, ignoreCase)
@staticmethod
def removeStopwords(ls):
return [w for w in ls if w.lower() not in Matcher.stopwords]
# CONSTANTS
BLEU_THRESHOLD = 0.4
LEXICAL_THRESHOLD = 0.5 # Note: changing this value didn't change the ordering of the tested systems
stopwords = stopwords.words('english') + list(string.punctuation) |