predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
where NN I-NP O
the NN I-NP O
cuisine NN I-NP I-FLT
is NN I-NP I-FLO
chinese NN I-NP O
for NN I-NP O
the NN I-NP O
next NN I-NP O
week NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
where NN I-NP O
the NN I-NP O
preferred NN I-NP I-FLT
medium NN I-NP I-FLT
of NN I-NP I-FLT
order NN I-NP I-FLT
is NN I-NP I-FLO
online NN I-NP O
within NN I-NP O
tomorrow NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
where NN I-NP O
the NN I-NP O
customer NN I-NP I-FLT
is NN I-NP I-FLO
married NN I-NP I-FLT
and NN I-NP O
monthly NN I-NP I-FLT
income NN I-NP I-FLT
is NN I-NP I-FLO
greater NN I-NP I-FLO
than NN I-NP I-FLO
two NN I-NP O
thousand NN I-NP O
within NN I-NP O
next NN I-NP O
two NN I-NP O
weeks NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
average NN I-NP I-AGG
reduction NN I-NP O
of NN I-NP O
order NN I-NP I-ENT
because NN I-NP O
of NN I-NP O
poor NN I-NP I-FLT
hygiene NN I-NP I-FLT
and NN I-NP O
health NN I-NP I-FLT
concern NN I-NP I-FLT

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ATR
placed NN I-NP I-ATR
by NN I-NP I-ATR
mistake NN I-NP I-ATR
when NN I-NP O
the NN I-NP O
customer NN I-NP O
lives NN I-NP I-FLT
in NN I-NP I-FLT
a NN I-NP I-FLT
busy NN I-NP I-FLT
location NN I-NP I-FLT
for NN I-NP O
the NN I-NP O
next NN I-NP O
week NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ATR
placed NN I-NP I-ATR
by NN I-NP I-ATR
mistake NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
age NN I-NP I-FLT
of NN I-NP I-FLT
a NN I-NP I-FLT
customer NN I-NP I-FLT
is NN I-NP I-FLO
less NN I-NP I-FLO
than NN I-NP I-FLO
21 NN I-NP I-NUM
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
missing NN I-NP I-ATR
item NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
gender NN I-NP I-FLT
of NN I-NP I-FLT
a NN I-NP I-FLT
customer NN I-NP I-FLT
is NN I-NP I-FLO
female NN I-NP O
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ATR
placed NN I-NP I-ATR
by NN I-NP I-ATR
mistake NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
occupation NN I-NP I-FLT
of NN I-NP I-FLT
the NN I-NP I-FLT
customer NN I-NP I-FLT
is NN I-NP I-FLO
job NN I-NP O
holder NN I-NP O
for NN I-NP O
tomorrow NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
high NN I-NP I-ATR
quality NN I-NP I-ATR
package NN I-NP I-ATR
order NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
customers NN I-NP O
monthly NN I-NP I-FLT
income NN I-NP I-FLT
is NN I-NP I-FLO
greater NN I-NP I-FLO
than NN I-NP I-FLO
three NN I-NP O
thousand NN I-NP O
within NN I-NP O
next NN I-NP O
two NN I-NP O
weeks NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
average NN I-NP I-AGG
accurately NN I-NP I-ATR
located NN I-NP I-ATR
on NN I-NP I-ATR
google NN I-NP I-ATR
maps NN I-NP I-ATR
order NN I-NP I-ENT
where NN I-NP O
the NN I-NP O
customers NN I-NP O
education NN I-NP I-FLT
qualification NN I-NP I-FLT
is NN I-NP I-FLO
high NN I-NP O
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ATR
with NN I-NP I-ATR
good NN I-NP I-ATR
food NN I-NP I-ATR
quality NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
family NN I-NP I-FLT
size NN I-NP I-FLT
is NN I-NP I-FLO
more NN I-NP I-FLO
than NN I-NP I-FLO
four NN I-NP O

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
with NN I-NP O
medium NN I-NP I-ATR
preference NN I-NP I-ATR
is NN I-NP I-FLO
online NN I-NP O
where NN I-NP O
age NN I-NP I-FLT
of NN I-NP I-FLT
customer NN I-NP I-FLT
is NN I-NP I-FLO
more NN I-NP I-FLO
than NN I-NP I-FLO
40 NN I-NP O
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
with NN I-NP O
medium NN I-NP I-ATR
preference NN I-NP I-ATR
is NN I-NP I-FLO
online NN I-NP O
where NN I-NP O
type NN I-NP I-FLT
of NN I-NP I-FLT
meal NN I-NP I-FLT
preferred NN I-NP I-FLT
is NN I-NP I-FLO
family NN I-NP I-FLT
size NN I-NP I-FLT
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
where NN I-NP O
the NN I-NP O
preferred NN I-NP I-FLT
cuisine NN I-NP I-FLT
is NN I-NP I-FLO
Indian NN I-NP O
for NN I-NP O
the NN I-NP O
next NN I-NP O
week NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
where NN I-NP O
there NN I-NP O
is NN I-NP I-FLO
ease NN I-NP I-FLT
of NN I-NP I-FLT
online NN I-NP I-FLT
order NN I-NP I-FLT
for NN I-NP O
the NN I-NP O
next NN I-NP O
week NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
where NN I-NP O
the NN I-NP O
number NN I-NP I-FLT
of NN I-NP I-FLT
choice NN I-NP I-FLT
of NN I-NP I-FLT
restaurant NN I-NP I-FLT
is NN I-NP I-FLO
more NN I-NP I-FLO
than NN I-NP I-FLO
three NN I-NP O
for NN I-NP O
the NN I-NP O
next NN I-NP O
week NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
with NN I-NP O
easy NN I-NP I-ATR
payment NN I-NP I-ATR
option NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
preferred NN I-NP I-FLT
cuisine NN I-NP I-FLT
is NN I-NP I-FLO
Chinese NN I-NP O
for NN I-NP O
the NN I-NP O
next NN I-NP O
week NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ATR
with NN I-NP I-ATR
offers NN I-NP I-ATR
and NN I-NP I-ATR
discounts NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
age NN I-NP I-FLT
of NN I-NP I-FLT
a NN I-NP I-FLT
customer NN I-NP I-FLT
is NN I-NP I-FLO
less NN I-NP I-FLO
than NN I-NP I-FLO
21 NN I-NP I-NUM
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ATR
with NN I-NP I-ATR
good NN I-NP I-ATR
food NN I-NP I-ATR
quality NN I-NP I-ATR
where NN I-NP O
age NN I-NP I-FLT
of NN I-NP I-FLT
customer NN I-NP I-FLT
is NN I-NP I-FLO
more NN I-NP I-FLO
than NN I-NP I-FLO
40 NN I-NP O
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ATR
delivered NN I-NP I-ATR
with NN I-NP I-ATR
good NN I-NP I-ATR
tracking NN I-NP I-ATR
system NN I-NP I-ATR
where NN I-NP O
age NN I-NP I-FLT
of NN I-NP I-FLT
customer NN I-NP I-FLT
is NN I-NP I-FLO
more NN I-NP I-FLO
than NN I-NP I-FLO
30 NN I-NP O
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
maximum NN I-NP I-AGG
reduction NN I-NP O
of NN I-NP O
order NN I-NP I-ENT
for NN I-NP O
self NN I-NP I-ATR
cooking NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
family NN I-NP I-FLT
size NN I-NP I-FLT
is NN I-NP I-FLO
more NN I-NP I-FLO
than NN I-NP I-FLO
four NN I-NP O
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
maximum NN I-NP I-AGG
reduction NN I-NP O
of NN I-NP O
order NN I-NP I-ENT
for NN I-NP O
health NN I-NP I-ATR
concern NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
family NN I-NP I-FLT
size NN I-NP I-FLT
is NN I-NP I-FLO
more NN I-NP I-FLO
than NN I-NP I-FLO
four NN I-NP O
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
average NN I-NP I-AGG
cancelled NN I-NP O
order NN I-NP I-ENT
for NN I-NP O
late NN I-NP I-ATR
delivery NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
age NN I-NP I-FLT
of NN I-NP I-FLT
a NN I-NP I-FLT
customer NN I-NP I-FLT
is NN I-NP I-FLO
less NN I-NP I-FLO
than NN I-NP I-FLO
21 NN I-NP I-NUM
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
reduced NN I-NP O
for NN I-NP O
poor NN I-NP I-ATR
hygiene NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
occupation NN I-NP I-FLT
of NN I-NP I-FLT
the NN I-NP I-FLT
customer NN I-NP I-FLT
is NN I-NP I-FLO
job NN I-NP O
holder NN I-NP O
for NN I-NP O
tomorrow NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
reduced NN I-NP O
for NN I-NP O
bad NN I-NP I-ATR
past NN I-NP I-ATR
experience NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
occupation NN I-NP I-FLT
of NN I-NP I-FLT
the NN I-NP I-FLT
customer NN I-NP I-FLT
is NN I-NP I-FLO
job NN I-NP O
holder NN I-NP O
for NN I-NP O
tomorrow NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
reduced NN I-NP O
for NN I-NP O
unavailability NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
family NN I-NP I-FLT
size NN I-NP I-FLT
is NN I-NP I-FLO
more NN I-NP I-FLO
than NN I-NP I-FLO
four NN I-NP O
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
reduced NN I-NP O
for NN I-NP O
being NN I-NP I-ATR
unaffordable NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
age NN I-NP I-FLT
of NN I-NP I-FLT
a NN I-NP I-FLT
customer NN I-NP I-FLT
is NN I-NP I-FLO
less NN I-NP I-FLO
than NN I-NP I-FLO
21 NN I-NP I-NUM
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
delay NN I-NP I-ATR
of NN I-NP I-ATR
delivery NN I-NP I-ATR
person NN I-NP I-ATR
getting NN I-NP I-ATR
assigned NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
customers NN I-NP O
residence NN I-NP I-FLT
is NN I-NP I-FLO
in NN I-NP I-FLT
busy NN I-NP I-FLT
location NN I-NP I-FLT
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
delay NN I-NP I-ATR
of NN I-NP I-ATR
delivery NN I-NP I-ATR
person NN I-NP I-ATR
picking NN I-NP I-ATR
up NN I-NP I-ATR
food NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
customers NN I-NP O
residence NN I-NP I-FLT
is NN I-NP I-FLO
in NN I-NP I-FLT
busy NN I-NP I-FLT
location NN I-NP I-FLT
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
delay NN I-NP I-ATR
of NN I-NP I-ATR
delivery NN I-NP I-ATR
person NN I-NP I-ATR
getting NN I-NP I-ATR
assigned NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
customers NN I-NP I-FLT
location NN I-NP I-FLT
on NN I-NP I-FLT
google NN I-NP I-FLT
map NN I-NP I-FLT
is NN I-NP I-FLO
not NN I-NP O
accurate NN I-NP O
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
delay NN I-NP I-ATR
of NN I-NP I-ATR
delivery NN I-NP I-ATR
person NN I-NP I-ATR
picking NN I-NP I-ATR
up NN I-NP I-ATR
food NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
delivery NN I-NP I-FLT
persons NN I-NP I-FLT
ability NN I-NP I-FLT
is NN I-NP I-FLO
not NN I-NP O
good NN I-NP O
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ATR
with NN I-NP I-ATR
good NN I-NP I-ATR
reviews NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
road NN I-NP I-FLT
condition NN I-NP I-FLT
is NN I-NP I-FLO
good NN I-NP O
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ATR
with NN I-NP I-ATR
good NN I-NP I-ATR
quantity NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
customers NN I-NP O
residence NN I-NP I-FLT
is NN I-NP I-FLO
in NN I-NP I-FLT
busy NN I-NP I-FLT
location NN I-NP I-FLT
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ATR
with NN I-NP I-ATR
good NN I-NP I-ATR
quantity NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
family NN I-NP I-FLT
size NN I-NP I-FLT
is NN I-NP I-FLO
more NN I-NP I-FLO
than NN I-NP I-FLO
four NN I-NP O
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ATR
time NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
road NN I-NP I-FLT
condition NN I-NP I-FLT
is NN I-NP I-FLO
good NN I-NP O
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ATR
time NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
road NN I-NP I-FLT
condition NN I-NP I-FLT
is NN I-NP I-FLO
not NN I-NP O
good NN I-NP O
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ATR
time NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
quantity NN I-NP I-FLT
is NN I-NP I-FLO
low NN I-NP I-FLT
and NN I-NP O
delivery NN I-NP I-FLT
time NN I-NP I-FLT
is NN I-NP I-FLO
low NN I-NP I-FLT
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
average NN I-NP I-AGG
number NN I-NP I-ATR
of NN I-NP I-ATR
calls NN I-NP I-ATR
made NN I-NP I-ATR
by NN I-NP I-ATR
delivery NN I-NP I-ATR
captain NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
delivery NN I-NP I-FLT
persons NN I-NP I-FLT
ability NN I-NP I-FLT
is NN I-NP I-FLO
not NN I-NP O
good NN I-NP O
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
average NN I-NP I-AGG
number NN I-NP I-ATR
of NN I-NP I-ATR
calls NN I-NP I-ATR
made NN I-NP I-ATR
by NN I-NP I-ATR
delivery NN I-NP I-ATR
captain NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
customers NN I-NP I-FLT
location NN I-NP I-FLT
on NN I-NP I-FLT
google NN I-NP I-FLT
map NN I-NP I-FLT
is NN I-NP I-FLO
not NN I-NP O
accurate NN I-NP O
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
average NN I-NP I-AGG
number NN I-NP I-ATR
of NN I-NP I-ATR
calls NN I-NP I-ATR
made NN I-NP I-ATR
by NN I-NP I-ATR
delivery NN I-NP I-ATR
captain NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
customers NN I-NP O
residence NN I-NP I-FLT
is NN I-NP I-FLO
in NN I-NP I-FLT
busy NN I-NP I-FLT
location NN I-NP I-FLT
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

I NN I-NP O
want NN I-NP O
to NN I-NP I-FLO
know NN I-NP O
the NN I-NP O
average NN I-NP I-AGG
temperature NN I-NP I-ATR
of NN I-NP I-ATR
food NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
quantity NN I-NP I-FLT
is NN I-NP I-FLO
low NN I-NP I-FLT
and NN I-NP O
delivery NN I-NP I-FLT
time NN I-NP I-FLT
is NN I-NP I-FLO
low NN I-NP I-FLT
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

I NN I-NP O
want NN I-NP O
to NN I-NP I-FLO
know NN I-NP O
the NN I-NP O
average NN I-NP I-AGG
temperature NN I-NP I-ATR
of NN I-NP I-ATR
food NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
customers NN I-NP O
residence NN I-NP O
area NN I-NP O
road NN I-NP I-FLT
condition NN I-NP I-FLT
is NN I-NP I-FLO
good NN I-NP O
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
increased NN I-NP O
for NN I-NP O
politeness NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
occupation NN I-NP I-FLT
of NN I-NP I-FLT
the NN I-NP I-FLT
customer NN I-NP I-FLT
is NN I-NP I-FLO
job NN I-NP O
holder NN I-NP O
for NN I-NP O
tomorrow NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
increased NN I-NP O
for NN I-NP O
freshness NN I-NP I-ATR
of NN I-NP I-ATR
food NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
occupation NN I-NP I-FLT
of NN I-NP I-FLT
the NN I-NP I-FLT
customer NN I-NP I-FLT
is NN I-NP I-FLO
job NN I-NP O
holder NN I-NP O
for NN I-NP O
tomorrow NN I-NP I-PRW

predict NN I-NP O
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meal NN I-NP I-FLT
preferred NN I-NP I-FLT
is NN I-NP I-FLO
family NN I-NP I-FLT
size NN I-NP I-FLT
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
with NN I-NP O
medium NN I-NP I-ATR
preference NN I-NP I-ATR
is NN I-NP I-FLO
online NN I-NP O
where NN I-NP O
type NN I-NP I-FLT
of NN I-NP I-FLT
meal NN I-NP I-FLT
preferred NN I-NP I-FLT
is NN I-NP I-FLO
family NN I-NP I-FLT
size NN I-NP I-FLT
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
with NN I-NP O
medium NN I-NP I-ATR
preference NN I-NP I-ATR
is NN I-NP I-FLO
online NN I-NP O
where NN I-NP O
type NN I-NP I-FLT
of NN I-NP I-FLT
meal NN I-NP I-FLT
preferred NN I-NP I-FLT
is NN I-NP I-FLO
family NN I-NP I-FLT
size NN I-NP I-FLT
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
where NN I-NP O
the NN I-NP O
preferred NN I-NP I-FLT
cuisine NN I-NP I-FLT
is NN I-NP I-FLO
Italian NN I-NP O
for NN I-NP O
the NN I-NP O
next NN I-NP O
week NN I-NP I-PRW

I NN I-NP O
want NN I-NP O
to NN I-NP I-FLO
know NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
where NN I-NP O
the NN I-NP O
preferred NN I-NP I-FLT
cuisine NN I-NP I-FLT
is NN I-NP I-FLO
Italian NN I-NP O
for NN I-NP O
the NN I-NP O
next NN I-NP O
week NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
where NN I-NP O
the NN I-NP O
preferred NN I-NP I-FLT
cuisine NN I-NP I-FLT
is NN I-NP I-FLO
Italian NN I-NP O
for NN I-NP O
the NN I-NP O
next NN I-NP O
week NN I-NP I-PRW

Help NN I-NP O
me NN I-NP O
find NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
where NN I-NP O
there NN I-NP O
is NN I-NP I-FLO
ease NN I-NP I-FLT
of NN I-NP I-FLT
online NN I-NP I-FLT
order NN I-NP I-FLT
for NN I-NP O
the NN I-NP O
next NN I-NP O
week NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
where NN I-NP O
there NN I-NP O
is NN I-NP I-FLO
ease NN I-NP I-FLT
of NN I-NP I-FLT
online NN I-NP I-FLT
order NN I-NP I-FLT
for NN I-NP O
the NN I-NP O
next NN I-NP O
week NN I-NP I-PRW

I NN I-NP O
want NN I-NP O
to NN I-NP I-FLO
know NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
where NN I-NP O
there NN I-NP O
is NN I-NP I-FLO
ease NN I-NP I-FLT
of NN I-NP I-FLT
online NN I-NP I-FLT
order NN I-NP I-FLT
for NN I-NP O
the NN I-NP O
next NN I-NP O
week NN I-NP I-PRW

Help NN I-NP O
me NN I-NP O
find NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
where NN I-NP O
the NN I-NP O
number NN I-NP I-FLT
of NN I-NP I-FLT
choice NN I-NP I-FLT
of NN I-NP I-FLT
restaurant NN I-NP I-FLT
is NN I-NP I-FLO
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three NN I-NP O
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the NN I-NP O
next NN I-NP O
week NN I-NP I-PRW

Help NN I-NP O
me NN I-NP O
find NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
where NN I-NP O
the NN I-NP O
number NN I-NP I-FLT
of NN I-NP I-FLT
choice NN I-NP I-FLT
of NN I-NP I-FLT
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is NN I-NP I-FLO
more NN I-NP I-FLO
than NN I-NP I-FLO
three NN I-NP O
for NN I-NP O
the NN I-NP O
next NN I-NP O
week NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
where NN I-NP O
the NN I-NP O
number NN I-NP I-FLT
of NN I-NP I-FLT
choice NN I-NP I-FLT
of NN I-NP I-FLT
restaurant NN I-NP I-FLT
is NN I-NP I-FLO
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three NN I-NP O
for NN I-NP O
the NN I-NP O
next NN I-NP O
week NN I-NP I-PRW

I NN I-NP O
want NN I-NP O
to NN I-NP I-FLO
know NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
with NN I-NP O
easy NN I-NP I-ATR
payment NN I-NP I-ATR
option NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
preferred NN I-NP I-FLT
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is NN I-NP I-FLO
Italian NN I-NP O
for NN I-NP O
the NN I-NP O
next NN I-NP O
week NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
with NN I-NP O
easy NN I-NP I-ATR
payment NN I-NP I-ATR
option NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
preferred NN I-NP I-FLT
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is NN I-NP I-FLO
Italian NN I-NP O
for NN I-NP O
the NN I-NP O
next NN I-NP O
week NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
with NN I-NP O
easy NN I-NP I-ATR
payment NN I-NP I-ATR
option NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
preferred NN I-NP I-FLT
cuisine NN I-NP I-FLT
is NN I-NP I-FLO
Italian NN I-NP O
for NN I-NP O
the NN I-NP O
next NN I-NP O
week NN I-NP I-PRW

I NN I-NP O
want NN I-NP O
to NN I-NP I-FLO
know NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ATR
with NN I-NP I-ATR
offers NN I-NP I-ATR
and NN I-NP I-ATR
discounts NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
age NN I-NP I-FLT
of NN I-NP I-FLT
a NN I-NP I-FLT
customer NN I-NP I-FLT
is NN I-NP I-FLO
less NN I-NP I-FLO
than NN I-NP I-FLO
21 NN I-NP I-NUM
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ATR
with NN I-NP I-ATR
offers NN I-NP I-ATR
and NN I-NP I-ATR
discounts NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
age NN I-NP I-FLT
of NN I-NP I-FLT
a NN I-NP I-FLT
customer NN I-NP I-FLT
is NN I-NP I-FLO
less NN I-NP I-FLO
than NN I-NP I-FLO
21 NN I-NP I-NUM
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

I NN I-NP O
want NN I-NP O
to NN I-NP I-FLO
know NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ATR
with NN I-NP I-ATR
offers NN I-NP I-ATR
and NN I-NP I-ATR
discounts NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
age NN I-NP I-FLT
of NN I-NP I-FLT
a NN I-NP I-FLT
customer NN I-NP I-FLT
is NN I-NP I-FLO
less NN I-NP I-FLO
than NN I-NP I-FLO
21 NN I-NP I-NUM
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ATR
with NN I-NP I-ATR
good NN I-NP I-ATR
food NN I-NP I-ATR
quality NN I-NP I-ATR
where NN I-NP O
age NN I-NP I-FLT
of NN I-NP I-FLT
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is NN I-NP I-FLO
more NN I-NP I-FLO
than NN I-NP I-FLO
40 NN I-NP O
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

Help NN I-NP O
me NN I-NP O
find NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ATR
with NN I-NP I-ATR
good NN I-NP I-ATR
food NN I-NP I-ATR
quality NN I-NP I-ATR
where NN I-NP O
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of NN I-NP I-FLT
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is NN I-NP I-FLO
more NN I-NP I-FLO
than NN I-NP I-FLO
40 NN I-NP O
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

Help NN I-NP O
me NN I-NP O
find NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ATR
with NN I-NP I-ATR
good NN I-NP I-ATR
food NN I-NP I-ATR
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where NN I-NP O
age NN I-NP I-FLT
of NN I-NP I-FLT
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is NN I-NP I-FLO
more NN I-NP I-FLO
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40 NN I-NP O
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ATR
delivered NN I-NP I-ATR
with NN I-NP I-ATR
good NN I-NP I-ATR
tracking NN I-NP I-ATR
system NN I-NP I-ATR
where NN I-NP O
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of NN I-NP I-FLT
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is NN I-NP I-FLO
more NN I-NP I-FLO
than NN I-NP I-FLO
30 NN I-NP O
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

I NN I-NP O
want NN I-NP O
to NN I-NP I-FLO
know NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ATR
delivered NN I-NP I-ATR
with NN I-NP I-ATR
good NN I-NP I-ATR
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system NN I-NP I-ATR
where NN I-NP O
age NN I-NP I-FLT
of NN I-NP I-FLT
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is NN I-NP I-FLO
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30 NN I-NP O
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

Can NN I-NP O
you NN I-NP O
help NN I-NP O
me NN I-NP O
find NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ATR
delivered NN I-NP I-ATR
with NN I-NP I-ATR
good NN I-NP I-ATR
tracking NN I-NP I-ATR
system NN I-NP I-ATR
where NN I-NP O
age NN I-NP I-FLT
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is NN I-NP I-FLO
more NN I-NP I-FLO
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30 NN I-NP O
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

I NN I-NP O
want NN I-NP O
to NN I-NP I-FLO
know NN I-NP O
the NN I-NP O
mean NN I-NP I-AGG
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order NN I-NP I-ENT
for NN I-NP O
self NN I-NP I-ATR
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where NN I-NP O
the NN I-NP O
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size NN I-NP I-FLT
is NN I-NP I-FLO
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four NN I-NP O
within NN I-NP O
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month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
average NN I-NP I-AGG
reduction NN I-NP O
of NN I-NP O
order NN I-NP I-ENT
for NN I-NP O
self NN I-NP I-ATR
cooking NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
family NN I-NP I-FLT
size NN I-NP I-FLT
is NN I-NP I-FLO
more NN I-NP I-FLO
than NN I-NP I-FLO
four NN I-NP O
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

I NN I-NP O
want NN I-NP O
to NN I-NP I-FLO
know NN I-NP O
the NN I-NP O
min NN I-NP I-AGG
reduction NN I-NP O
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order NN I-NP I-ENT
for NN I-NP O
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cooking NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
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size NN I-NP I-FLT
is NN I-NP I-FLO
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four NN I-NP O
within NN I-NP O
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month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
min NN I-NP I-AGG
reduction NN I-NP O
of NN I-NP O
order NN I-NP I-ENT
for NN I-NP O
health NN I-NP I-ATR
concern NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
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size NN I-NP I-FLT
is NN I-NP I-FLO
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four NN I-NP O
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
average NN I-NP I-AGG
reduction NN I-NP O
of NN I-NP O
order NN I-NP I-ENT
for NN I-NP O
health NN I-NP I-ATR
concern NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
family NN I-NP I-FLT
size NN I-NP I-FLT
is NN I-NP I-FLO
more NN I-NP I-FLO
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four NN I-NP O
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

I NN I-NP O
want NN I-NP O
to NN I-NP I-FLO
know NN I-NP O
the NN I-NP O
mean NN I-NP I-AGG
reduction NN I-NP O
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order NN I-NP I-ENT
for NN I-NP O
health NN I-NP I-ATR
concern NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
family NN I-NP I-FLT
size NN I-NP I-FLT
is NN I-NP I-FLO
more NN I-NP I-FLO
than NN I-NP I-FLO
four NN I-NP O
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
max NN I-NP I-AGG
cancelled NN I-NP O
order NN I-NP I-ENT
for NN I-NP O
late NN I-NP I-ATR
delivery NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
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of NN I-NP I-FLT
a NN I-NP I-FLT
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is NN I-NP I-FLO
less NN I-NP I-FLO
than NN I-NP I-FLO
21 NN I-NP I-NUM
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

Can NN I-NP O
you NN I-NP O
help NN I-NP O
me NN I-NP O
find NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
cancelled NN I-NP O
order NN I-NP I-ENT
for NN I-NP O
late NN I-NP I-ATR
delivery NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
age NN I-NP I-FLT
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a NN I-NP I-FLT
customer NN I-NP I-FLT
is NN I-NP I-FLO
less NN I-NP I-FLO
than NN I-NP I-FLO
21 NN I-NP I-NUM
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

I NN I-NP O
want NN I-NP O
to NN I-NP I-FLO
know NN I-NP O
the NN I-NP O
mean NN I-NP I-AGG
cancelled NN I-NP O
order NN I-NP I-ENT
for NN I-NP O
late NN I-NP I-ATR
delivery NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
age NN I-NP I-FLT
of NN I-NP I-FLT
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customer NN I-NP I-FLT
is NN I-NP I-FLO
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than NN I-NP I-FLO
21 NN I-NP I-NUM
for NN I-NP O
the NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

I NN I-NP O
want NN I-NP O
to NN I-NP I-FLO
know NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
reduced NN I-NP O
for NN I-NP O
poor NN I-NP I-ATR
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where NN I-NP O
the NN I-NP O
occupation NN I-NP I-FLT
of NN I-NP I-FLT
the NN I-NP I-FLT
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job NN I-NP O
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for NN I-NP O
tomorrow NN I-NP I-PRW

I NN I-NP O
want NN I-NP O
to NN I-NP I-FLO
know NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
reduced NN I-NP O
for NN I-NP O
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where NN I-NP O
the NN I-NP O
occupation NN I-NP I-FLT
of NN I-NP I-FLT
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for NN I-NP O
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predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
reduced NN I-NP O
for NN I-NP O
poor NN I-NP I-ATR
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where NN I-NP O
the NN I-NP O
occupation NN I-NP I-FLT
of NN I-NP I-FLT
the NN I-NP I-FLT
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job NN I-NP O
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for NN I-NP O
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predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
reduced NN I-NP O
for NN I-NP O
bad NN I-NP I-ATR
past NN I-NP I-ATR
experience NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
occupation NN I-NP I-FLT
of NN I-NP I-FLT
the NN I-NP I-FLT
customer NN I-NP I-FLT
is NN I-NP I-FLO
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for NN I-NP O
tomorrow NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
reduced NN I-NP O
for NN I-NP O
bad NN I-NP I-ATR
past NN I-NP I-ATR
experience NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
occupation NN I-NP I-FLT
of NN I-NP I-FLT
the NN I-NP I-FLT
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is NN I-NP I-FLO
job NN I-NP O
holder NN I-NP O
for NN I-NP O
tomorrow NN I-NP I-PRW

I NN I-NP O
want NN I-NP O
to NN I-NP I-FLO
know NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
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for NN I-NP O
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past NN I-NP I-ATR
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where NN I-NP O
the NN I-NP O
occupation NN I-NP I-FLT
of NN I-NP I-FLT
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Can NN I-NP O
you NN I-NP O
help NN I-NP O
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the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
reduced NN I-NP O
for NN I-NP O
unavailability NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
family NN I-NP I-FLT
size NN I-NP I-FLT
is NN I-NP I-FLO
more NN I-NP I-FLO
than NN I-NP I-FLO
four NN I-NP O
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
reduced NN I-NP O
for NN I-NP O
unavailability NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
family NN I-NP I-FLT
size NN I-NP I-FLT
is NN I-NP I-FLO
more NN I-NP I-FLO
than NN I-NP I-FLO
four NN I-NP O
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

predict NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
reduced NN I-NP O
for NN I-NP O
unavailability NN I-NP I-ATR
where NN I-NP O
the NN I-NP O
family NN I-NP I-FLT
size NN I-NP I-FLT
is NN I-NP I-FLO
more NN I-NP I-FLO
than NN I-NP I-FLO
four NN I-NP O
within NN I-NP O
next NN I-NP O
month NN I-NP I-PRW

I NN I-NP O
want NN I-NP O
to NN I-NP I-FLO
know NN I-NP O
the NN I-NP O
total NN I-NP I-AGG
order NN I-NP I-ENT
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predict NN I-NP O
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predict NN I-NP O
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predict NN I-NP O
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