<s>
In	O
machine	O
learning	O
,	O
sequence	B-General_Concept
labeling	I-General_Concept
is	O
a	O
type	O
of	O
pattern	O
recognition	O
task	O
that	O
involves	O
the	O
algorithmic	O
assignment	O
of	O
a	O
categorical	O
label	O
to	O
each	O
member	O
of	O
a	O
sequence	O
of	O
observed	O
values	O
.	O
</s>
<s>
A	O
common	O
example	O
of	O
a	O
sequence	B-General_Concept
labeling	I-General_Concept
task	O
is	O
part	O
of	O
speech	O
tagging	O
,	O
which	O
seeks	O
to	O
assign	O
a	O
part	O
of	O
speech	O
to	O
each	O
word	O
in	O
an	O
input	O
sentence	O
or	O
document	O
.	O
</s>
<s>
Sequence	B-General_Concept
labeling	I-General_Concept
can	O
be	O
treated	O
as	O
a	O
set	O
of	O
independent	O
classification	B-General_Concept
tasks	O
,	O
one	O
per	O
member	O
of	O
the	O
sequence	O
.	O
</s>
<s>
Most	O
sequence	B-General_Concept
labeling	I-General_Concept
algorithms	O
are	O
probabilistic	O
in	O
nature	O
,	O
relying	O
on	O
statistical	O
inference	O
to	O
find	O
the	O
best	O
sequence	O
.	O
</s>
<s>
The	O
most	O
common	O
statistical	O
models	O
in	O
use	O
for	O
sequence	B-General_Concept
labeling	I-General_Concept
make	O
a	O
Markov	O
assumption	O
,	O
i.e.	O
</s>
<s>
This	O
leads	O
naturally	O
to	O
the	O
hidden	O
Markov	O
model	O
(	O
HMM	O
)	O
,	O
one	O
of	O
the	O
most	O
common	O
statistical	O
models	O
used	O
for	O
sequence	B-General_Concept
labeling	I-General_Concept
.	O
</s>
<s>
Other	O
common	O
models	O
in	O
use	O
are	O
the	O
maximum	B-General_Concept
entropy	I-General_Concept
Markov	I-General_Concept
model	I-General_Concept
and	O
conditional	B-General_Concept
random	I-General_Concept
field	I-General_Concept
.	O
</s>
