File size: 3,424 Bytes
b109984 |
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 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 |
spouse relation classification
# 9 labeling functions (weak_labels)
lfs = [
lf_husband_wife,
lf_husband_wife_left_window,
lf_same_last_name,
lf_married,
lf_familial_relationship,
lf_family_left_window,
lf_other_relationship,
lf_distant_supervision,
lf_distant_supervision_last_names,
]
# Labels
0 as Negative
1 as Positive
-1 as ABSTAIN
#### LF 1
# Check for the `spouse` words appearing between the person mentions
spouses = {"spouse", "wife", "husband", "ex-wife", "ex-husband"}
def lf_husband_wife(x, spouses):
return POSITIVE if len(spouses.intersection(set(x.between_tokens))) > 0 else ABSTAIN
#### LF 2
# Check for the `spouse` words appearing to the left of the person mentions
def lf_husband_wife_left_window(x, spouses):
if len(set(spouses).intersection(set(x.person1_left_tokens))) > 0:
return POSITIVE
elif len(set(spouses).intersection(set(x.person2_left_tokens))) > 0:
return POSITIVE
else:
return ABSTAIN
#### LF 3
# Check for the person mentions having the same last name
@labeling_function(pre=[get_person_last_names])
def lf_same_last_name(x):
p1_ln, p2_ln = x.person_lastnames
if p1_ln and p2_ln and p1_ln == p2_ln:
return POSITIVE
return ABSTAIN
#### LF 4
# Check for the word `married` between person mentions
@labeling_function()
def lf_married(x):
return POSITIVE if "married" in x.between_tokens else ABSTAIN
#### LF 5
# Check for words that refer to `family` relationships between the person mentions
family = {
"father",
"mother",
"sister",
"brother",
"son",
"daughter",
"grandfather",
"grandmother",
"uncle",
"aunt",
"cousin",
}
family = family.union({f + "-in-law" for f in family})
def lf_familial_relationship(x, family):
return NEGATIVE if len(family.intersection(set(x.between_tokens))) > 0 else ABSTAIN
#### LF 6
# Check for words that refer to `family` relationships to the left of the person mentions
def lf_family_left_window(x, family):
if len(set(family).intersection(set(x.person1_left_tokens))) > 0:
return NEGATIVE
elif len(set(family).intersection(set(x.person2_left_tokens))) > 0:
return NEGATIVE
else:
return ABSTAIN
#### LF 7
# Check for `other` relationship words between person mentions
other = {"boyfriend", "girlfriend", "boss", "employee", "secretary", "co-worker"}
def lf_other_relationship(x, other):
return NEGATIVE if len(other.intersection(set(x.between_tokens))) > 0 else ABSTAIN
#### LF 8
# Simple distant supervision labeling function via DBPedia
@labeling_function(resources=dict(known_spouses=known_spouses), pre=[get_person_text])
def lf_distant_supervision(x, known_spouses):
p1, p2 = x.person_names
if (p1, p2) in known_spouses or (p2, p1) in known_spouses:
return POSITIVE
else:
return ABSTAIN
#### LF 9
# Last name pairs for known spouses
last_names = set(
[
(last_name(x), last_name(y))
for x, y in known_spouses
if last_name(x) and last_name(y)
]
)
@labeling_function(resources=dict(last_names=last_names), pre=[get_person_last_names])
def lf_distant_supervision_last_names(x, last_names):
p1_ln, p2_ln = x.person_lastnames
return (
POSITIVE
if (p1_ln != p2_ln)
and ((p1_ln, p2_ln) in last_names or (p2_ln, p1_ln) in last_names)
else ABSTAIN
) |