NLP for CP
Addressing Constraint Programming with Natural Language Processing
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Correct
predictions are in
blue
. If we detect only a subset of a labelled sentence, we highlight the caught part as
blue
, the missing part
light blue.
False positives
are in
green
and
false negatives
are in
red
.
Problem Staff_Assignment — Constraint detection
A
fast-food
franchise
runs
from
8am
to
8pm
on
Monday
to
Friday
.
Each
working
day
is
divided
into
three
4-hour
shifts
:
breakfast
from
8am
to
12noon
,
lunch
from
12noon
to
4pm
,
dinner
from
4pm
to
8pm
.
Each
shift
demands
three
types
of
personnel
:
cook
,
cashier
,
and
cleaner
.
Each
of
its
personnel
is
capable
of
performing
a
subset
of
these
tasks
.
Some
people
may
not
be
available
on
certain
days
.
If
scheduled
,
each
person
works
8
hours
a
day
-LRB-
2
shifts
in
this
case
-RRB-
,
and
prefers
the
shifts
to
be
consecutive
.
Also
,
if
the
person
works
on
a
night
shift
,
he
or
she
prefers
not
to
work
on
the
morning
shift
the
next
day
.
The
objective
is
to
find
a
balanced
assignment
of
workers
to
shifts
that
minimizes
the
total
number
of
unfilled
slots
.
Problem Staff_Assignment — Detection of the decisions and objects to be modeled
A
fast-food
franchise
runs
from
8am
to
8pm
on
Monday
to
Friday
.
Each
working
day
is
divided
into
three
4-hour
shifts
:
breakfast
from
8am
to
12noon
,
lunch
from
12noon
to
4pm
,
dinner
from
4pm
to
8pm
.
Each
shift
demands
three
types
of
personnel
:
cook
,
cashier
,
and
cleaner
.
Each
of
its
personnel
is
capable
of
performing
a
subset
of
these
tasks
.
Some
people
may
not
be
available
on
certain
days
.
If
scheduled
,
each
person
works
8
hours
a
day
-LRB-
2
shifts
in
this
case
-RRB-
,
and
prefers
the
shifts
to
be
consecutive
.
Also
,
if
the
person
works
on
a
night
shift
,
he
or
she
prefers
not
to
work
on
the
morning
shift
the
next
day
.
The
objective
is
to
find
a
balanced
assignment
of
workers
to
shifts
that
minimizes
the
total
number
of
unfilled
slots
.
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