classification/spouse/readme.txt

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
    )