A model for epistemic modality and knowledge attribution
1. Epistemic
Modality
and
Knowledge
A5ribu9on:
Types
and
Features
Anita
de
Waard,
Elsevier
Labs
Henk
Pander
Maat,
UiL-‐OTS,
Utrecht
University
July
12,
2012
DSSD-‐2012,
ACL
Jeju
2. Epistemic
Modality
and
Knowledge
A5ribu9on:
Introduc9on:
– Why
is
epistemic
modality
interes9ng?
– Research
ques9ons
– Some
related
work
in
genre
studies,
linguis9cs,
CL
Methods
and
Results:
– A
taxonomy
of
types
and
markers
– In
defense
of
the
clause
as
a
unit
of
thought
– A
small
corpus
study
Conclusions
and
Applica9ons:
– Connec9ng
formal
representa9ons
to
text
– A
corpus
of
cita9ons
– Did
this
answer
our
research
ques9ons?
3. Latour,
1987:
[Y]ou
can
transform
a
fact
into
fic9on
or
a
fic9on
into
fact
just
by
adding
or
subtrac9ng
references
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
4. How
a
claim
becomes
a
fact:
• Voorhoeve
et
al.,
2006:
These
miRNAs
neutralize
p53-‐
mediated
CDK
inhibi9on,
possibly
through
direct
inhibi9on
of
the
expression
of
the
tumor
suppressor
LATS2.
• Kloosterman
and
Plasterk,
2006:
In
a
gene9c
screen,
miR-‐372
and
miR-‐373
were
found
to
allow
prolifera9on
of
primary
human
cells
that
express
oncogenic
RAS
and
ac9ve
p53,
possibly
by
inhibi9ng
the
tumor
suppressor
LATS2
(Voorhoeve
et
al.,
2006).
• Yabuta
et
al.,
2007:
[On
the
other
hand,]
two
miRNAs,
miRNA-‐372
and-‐373,
func9on
as
poten1al
novel
oncogenes
in
tes9cular
germ
cell
tumors
by
inhibi9on
of
LATS2
expression,
which
suggests
that
Lats2
is
an
important
tumor
suppressor
(Voorhoeve
et
al.,
2006).
• Okada
et
al.,
2011:
Two
oncogenic
miRNAs,
miR-‐372
and
miR-‐373,
directly
inhibit
the
expression
of
Lats2,
thereby
allowing
tumorigenic
growth
in
the
presence
of
p53
(Voorhoeve
et
al.,
2006).
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
5. Research
Ques9ons:
1. Can
we
find
a
model
for
epistemic
evalua1on
and
knowledge
a5ribu9on
to
describe
all
biological
statements
in
a
straighhorward
way?
2. If
yes:
can
we
detect
this
evalua9on
-‐
manually,
and
automa9cally?
3. Is
this
model
useful
for
examining
the
mechanism
of
hedging
erosion ,
does
it
show
how
a
claim
becomes
validated
aier
being
cited?
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
6. Related
work:
Genre
Studies
• Why
do
authors
hedge?
– Make
a
claim
‘pending
[…]
acceptance
in
the
community’
(Myers,
1989)
– ‘Create
A
Research
Space’
–
hedging
allows
authors
to
insert
themselves
into
the
discourse
in
a
community
(Swales,
1990)
– ‘the
strongest
claim
a
careful
researcher
can
make’
(Salager-‐Meyer,
1994)
– Types:
writer-‐oriented,
accuracy-‐oriented
and
reader-‐
oriented
hedges
(Hyland,
1994)
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
7. Related
work:
Linguis9cs
• How
do
authors
hedge?
– ‘Modifiers
of
Proposi9onal
Content’
-‐
kind,
degree
and
source
(Hengeveld/Mackenzie,
2008)
– Type
of
hypotaxis:
projec9on
vs.
embedding/expanding
(e.g.
Halliday
&
Ma5hiessen,
2004)
– Cogni9ve
linguis9cs:
‘grounding
elements
[…]
establish
an
epistemic
rela9onship
between
the
ground
and
the
profiled
thing…’
(Langacker,
2008)
– E.g.
finite
complements
make
‘The
subject
become(s)
the
object’
(Verhagen,
2007),
foregrounding
the
author:
‘we
hypothesized
that
nuclear
proteins
bind
to
exon
1’
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
8. Related
work:
CL
• How
do
we
find
hedges?
– Hedging
cues,
specula9ve
language,
modality/nega9on
(very
small
selec9on
–
see
many
more,
e.g.
by
Teufel
Morante,
Sporleder,
others!):
• (Light
et
al,
2004):
finding
specula9ve
language
• (Wilbur
et
al,
2006):
focus,
polarity,
certainty,
evidence,
and
direc9onality
• (Thompson
et
al,
2008):
level
of
specula9on,
type/source
of
the
evidence
and
level
of
certainty
– Sen9ment
detec9on
(e.g.
Kim
and
Hovy,
2004
a.m.o.):
• Holder
of
the
opinion,
strength,
polarity
as
‘mathema9cal
func9on’
ac9ng
on
main
proposi9onal
content
• S(P)
has
different
a5ributes:
strength,
polarity,
source,
etc.
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
9. Proposal:
taxonomy
of
epistemic
evalua9on/knowledge
a5ribu9on
For
a
Proposi9on
P,
an
epistemically
marked
clause
E
is
an
Evalua9on
of
P,
EV,
B,
S(P),
with:
V
=
Value:
3
=
Assumed
true,
2
=
Probable,
1
=
Possible,
0
=
Unknown,
(-‐
1=
possibly
untrue,
-‐
2
=
probably
untrue,
-‐3
=
assumed
untrue)
B
=
Basis:
Reasoning
Data
S
=
Source:
A
=
speaker
is
author
A,
explicit
IA
=
speaker
author,
A,
implicit
N
=
other
author
N,
explicit
NN
=
other
author
NN,
implicit
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
10. Some
examples:
Concept
Values
Example
Value
0
-‐
Lack
of
knowledge:
Thus,
it
remains
to
be
determined
if...
1
–
Hypothe9cal:
low
certainty
GATA-‐1
binding
to
exon
1
may
affect
transcrip1on
start
site
func1on
2
–
Dubita9ve:
higher
likelihood
but
sugges0ng
the
presence
of
lineage-‐specific
short
of
complete
certainty
elements.
3
–
Doxas9c:
complete
certainty,
the
1.6
kb
5'
flanking
region
of
CCR3
has
accepted/known/proven
fact
promoter
ac1vity
in
vivo.
Basis
R
–
Reasoning
Therefore,
one
can
argue…
D
–
Data
These
results
suggest…
0
–
Uniden9fied
Studies
report
that…
Source
A
-‐
Author:
Explicit
men9on
of
We
hypothesize
that…
author/current
paper
as
source
Fig
2a
shows
that…
N
-‐
Named
external
source,
either
…several
reports
have
documented
this
explicitly
or
as
a
reference
expression
[11-‐16,42].
IA
-‐
Implicit
a5ribu9on
to
the
author
Electrophore0c
mobility
shiB
analysis
revealed
that…
NN
–
Nameless
external
source
no
eosinophil-‐specific
transcrip1on
factors
have
been
reported…
0
–
No
source
of
knowledge
transcrip1on
factors
are
the
final
common
pathway
driving
differen1a1on
11. Epistemic
Markers
• Modal
auxiliary
verbs
(e.g.
can,
could,
might)
• Qualifying
adverbs
and
adjec9ves
(e.g.
interes1ngly,
possibly,
likely,
poten1al,
somewhat,
slightly,
powerful,
unknown,
undefined)
• References,
either
external
(e.g.
[Voorhoeve
et
al.,
2006] )
or
internal
(e.g.
See
fig.
2a ).
• Repor9ng/epistemic
verbs
(e.g.
suggest,
imply,
indicate,
show)
– either
within
the
clause:
These
results
suggest
that...
– or
in
a
subordinate
clause
governed
by
repor9ng-‐verb
matrix
clause
{These
results
suggest
that}
indeed,
this
represents
the
true
endogenous
ac1vity.
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
12. In
defense
of
the
clause
as
a
unit
of
thought:
• Argumenta9ve
zoning:
several
sentences
• Bio-‐events:
supra-‐
to
sub-‐
senten9al
• CORE-‐SC:
sentence
• My
discourse
segments:
clause
–
Elementary
Discourse
Units
(EDU)
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
13. Voorhoeve
et
al.,
(2006):
1. Importantly,
our
results
so
far
indicate
that
the
expression
of
miR-‐372&3
did
not
reduce
the
ac9vity
of
RASV12,
as
these
cells
were
s9ll
growing
faster
than
normal
cells
and
were
tumorigenic,
for
which
RAS
ac9vity
is
indispensable
(Hahn
et
al,
1999
and
Kolfschoten
et
al,
2005).
2. To
shed
more
light
on
this
aspect,
we
examined
the
effect
of
miR-‐372&3
expression
on
p53
ac9va9on
in
response
to
oncogenic
s9mula9on.
3. We
used
for
this
experiment
BJ/ET
cells
containing
p14ARFkd
because,
following
RASV12
treatment,
in
those
cells
p53
is
s9ll
ac9vated
but
more
clearly
stabilized
than
in
parental
BJ/ET
cells
(Voorhoeve
and
Agami,
2003),
resul9ng
in
a
sensi9zed
system
for
slight
altera9ons
in
p53
in
response
to
RASV12.
4. Figure
4A
shows
that
following
RASV12
s9mula9on,
p53
was
stabilized
and
ac9vated,
and
its
target
gene,
p21cip1,
was
induced
in
all
cases,
indica9ng
an
intact
p53
pathway
in
these
cells.
• More
than
one
‘thought
unit’
per
sentence.
• Verb
tense
changes
within
sentence
(several
9mes).
• A5ribu9on,
ac9ons/states,
and
preposi9ons
all
contained
within
a
sentence.
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
14. Voorhoeve
et
al.,
(2006):
1. Importantly,
our
results
so
far
indicate
that
the
expression
of
miR-‐372&3
did
not
reduce
the
ac9vity
of
RASV12,
as
these
cells
were
s9ll
growing
faster
than
normal
cells
and
were
tumorigenic,
for
which
RAS
ac9vity
is
indispensable
(Hahn
et
al,
1999
and
Kolfschoten
et
al,
2005).
2. To
shed
more
light
on
this
aspect,
we
examined
the
effect
of
miR-‐372&3
expression
on
p53
ac9va9on
in
response
to
oncogenic
s9mula9on.
3. We
used
for
this
experiment
BJ/ET
cells
containing
p14ARFkd
because,
following
RASV12
treatment,
in
those
cells
p53
is
s9ll
ac9vated
but
more
clearly
stabilized
than
in
parental
BJ/ET
cells
(Voorhoeve
and
Agami,
2003),
resul9ng
in
a
sensi9zed
system
for
slight
altera9ons
in
p53
in
response
to
RASV12.
4. Figure
4A
shows
that
following
RASV12
s9mula9on,
p53
was
stabilized
and
ac9vated,
and
its
target
gene,
p21cip1,
was
induced
in
all
cases,
indica9ng
an
intact
p53
pathway
in
these
cells.
Head:
premise,
mo9va9on,
Middle:
main
End:
interpreta9on,
elabora9on,
a5ribu9on
(matrix
clause)
biological
statement
a5ribu9on
(reference)
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
15. Voorhoeve
et
al.,
(2006):
1. Importantly,
our
results
so
far
indicate
that
the
expression
of
miR-‐372&3
did
not
reduce
the
ac9vity
of
RASV12,
as
these
cells
were
s9ll
growing
faster
than
normal
cells
and
were
tumorigenic,
for
which
RAS
ac9vity
is
indispensable
(Hahn
et
al,
1999
and
Kolfschoten
et
al,
2005).
2. To
shed
more
light
on
this
aspect,
we
examined
the
effect
of
miR-‐372&3
expression
on
p53
ac9va9on
in
response
to
oncogenic
s9mula9on.
3. We
used
for
this
experiment
BJ/ET
cells
containing
p14ARFkd
because,
following
RASV12
treatment,
in
those
cells
p53
is
s9ll
ac9vated
but
more
clearly
stabilized
than
in
parental
BJ/ET
cells
(Voorhoeve
and
Agami,
2003),
resul9ng
in
a
sensi9zed
system
for
slight
altera9ons
in
p53
in
response
to
RASV12.
4. Figure
4A
shows
that
following
RASV12
s9mula9on,
p53
was
stabilized
and
ac9vated,
and
its
target
gene,
p21cip1,
was
induced
in
all
cases,
indica9ng
an
intact
p53
pathway
in
these
cells.
Regulatory
Fact
Goal
Method
Result
Implica9on
clause
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
16. Small
corpus
study:
• Marked
up
of
clauses
with
modality
types
and
markers
for
one
full-‐text
biology
paper,
640
clauses
(Zimmermann,
2005)
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
17. Comments
on
small
corpus
study
• Very
preliminary:
one
paper
and
one
annotator!
• Not
always
completely
clear
of
value:
– ‘report’
vs.
‘demonstrate’?
– ‘Indicate’
vs.
‘show’?
• Some
clauses
don’t
have
a
modal
evalua9on,
– e.g.
Goal:
‘In
order
to
determine
if
this
region
had
promoter
ac9vity
in
vivo…’
– Method:
‘Nuclear
extracts
from
AML14.3D10
cells
were
incubated
with
the
radiolabelled
full-‐length
CCR3
exon
1
probe…’
• Some9mes
modality
changes
within
sentence:
– ‘It
has
been
reported
that
(value
=2)
the
5'
untranslated
exons
may
contain
sequences
that
facilitate
transcrip9on
of
the
gene.
(value
=
1)‘
– In
this
case,
iden9fy
at
a
clausal
level
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
18. Small
corpus
explora9on,
result:
Value
Modal
Repor1ng
Ruled
by
Adverbs/ References
None
Total
Aux
Verb
RV
Adjec1ves
Total
value
=
3
1
(0.5%)
81
(40%)
24
(12%)
7
(4%)
41
(20%)
47
(24%)
201
(100%)
Total
Value
=
2
29
(51%)
23
(40%)
1
(2%)
4(7%)
57
(100%)
Total
Value
=
1
9
(27%)
11
(33%)
11
(33%)
1
(3%)
1(3%)
33
(100%)
Total
Value
=
0
9
(64%)
3
(21%)
1
(7%)
1(7%)
14(100%)
Total
No
Modality
16
(37%)
3
(7%)
0
3(7%)
22(50%)
44
(100%)
Overall
Total
10
(2%)
146
(23%)
64
(10%)
10
(2%)
50
(8%)
69
(11%)
640(100%)
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
19. Repor9ng
verbs
vs.
epistemic
value:
Value
=
0
establish,
(remain
to
be)
elucidated,
(unknown)
be
(clear/useful),
(remain
to
be)
examined/determined,
describe,
make
difficult
to
infer,
report
Value
=
1
be
important,
consider,
expect,
hypothesize
(5x),
give
(hypothe9cal)
insight,
raise
possibility
that,
suspect,
think
Value
=
2
appear,
believe,
implicate
(2x),
imply,
indicate
(12x),
play
a
(probable)
role,
represent,
suggest
(18x),
validate
(2x),
Value
=
3
be
able/apparent/important
/posi9ve/visible,
compare
(presumed
true)
(2x),
confirm
(2x),
define,
demonstrate
(15x),
detect
(5x),
discover,
display
(3x),
eliminate,
find
(3x),
iden9fy
(4x),
know,
need,
note
(2x),
observe
(2x),
obtain
(success/
results-‐
3x),
prove
to
be,
refer,
report(2x),
reveal
(3x),
see(2x),
show(24x),
study,
view
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
20. Most
prevalent
clause
type:
These
results
suggest
that...
Adverb/Connec9ve
thus,
therefore,
together,
recently,
in
summary
Determiner/Pronoun
it,
this,
these,
we/our
Adjec9ve
previous,
future,
beer
Noun
phrase
data,
report,
study,
result(s);
method
or
reference
Modal
form
of
‘to
be’,
may,
remain
Adjec9ve
o_en,
recently,
generally
Verb
show,
obtain,
consider,
view,
reveal,
suggest,
hypothesize,
indicate,
believe
Preposi9on
that,
to
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
21. Applica9on:
connec9ng
text
to
formal
representa9ons
• Add
knowledge
value/basis/source
a5ribute
to
a
bio-‐event,
e.g.:
Biological
statement
with
epistemic
markup
Epistemic
evalua1on
Our
findings
reveal
that
miR-‐373
would
be
a
poten9al
Value
=
Probable
oncogene
and
it
par9cipates
in
the
carcinogenesis
of
Source
=
Author
human
esophageal
cancer
by
suppressing
LATS2
Basis
=
Data
expression.
Further
biochemical
characteriza9on
of
hMOBs
showed
Value
=
Presumed
true
that
only
hMOB1A
and
hMOB1B
interact
with
both
LATS1
Source
=
Reference
and
LATS2
in
vitro
and
in
vivo
[39].
Basis
=
Data
Moreover,
the
mechanisms
by
which
tumor
suppressor
Value
=
Possible
genes
are
inhibited
may
vary
between
tumors.
Source
=
Unknown
Basis
=
Unknown
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
22. E.g.
to
augment
Medscan
(Ariadne)
Biological
statement
with
Medscan/ MedScan
Analysis:
Epistemic
epistemic
markup
evalua1on
Furthermore,
we
present
evidence
that
IL-‐6
è
NUCB2
(nesfa1n-‐1)
Value
=
Probable
the
secre1on
of
nesfa0n-‐1
into
the
Rela9on:
MolTransport
Source
=
Author
culture
media
was
drama9cally
increased
Effect:
Posi9ve
Basis
=
Data
during
the
differen9a9on
of
3T3-‐L1
CellType:
Adipocytes
preadipocytes
into
adipocytes
(P
<
0.001)
Cell
Line:
3T3-‐L1
and
aier
treatments
with
TNF-‐alpha,
IL-‐6,
insulin,
and
dexamethasone
(P
<
0.01).
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
23. Or
BEL
(Biological
Exchange
Language):
Biological
statement
with
BEL
representa1on:
Epistemic
BEL/
epistemic
markup
evalua1on
These
miRNAs
neutralize
p53-‐ Increased
abundance
of
miR-‐372
Value
=
Possible
decreases:
Increased
ac1vity
of
TP53
mediated
CDK
inhibi1on,
Source
=
decreases
ac1vity
of
CDK
protein
family
possibly
through
direct
r(MIR:miR-‐372)
-‐| Unknown
inhibi1on
of
the
expression
of
(tscript(p(HUGO:Trp53))
-‐|
Basis
=
the
tumor-‐suppressor
LATS2.
kin(p(PFH:”CDK
Family”)))
Unknown
Increased
abundance
of
miR-‐372
decreases
abundance
of
LATS2
r(MIR:miR-‐372)
-‐|
r(HUGO:LATS2)
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
24. Implementa9on:
can
we
find
this
in
text?
• Work
on
Claimed
Knowledge
updates
was
a
first
a5empt…
• Probably:
– Need
be5er
clause
taggers
(e.g.
Feng
and
Hirst,
2012)
– Need
be5er
verb
form
detec9on
– Need
more
appropriate
seman9c
verb
classes
• Hope
to
piggyback
on
bio-‐event
detec9on.
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
25. Following
a
claim
as
it
becomes
a
fact?
• TAC
Challenge
2013:
find
most
appropriate
cited
‘zones’
in
reference
papers,
given
the
reference
• With
NIST
and
U
Colorado:
Create
a
goal
standard:
20
papers
in
biology
with
10
ci9ng
papers
each
• Perhaps
we
can
trace
a
trail
of
3
‘genera9ons’
of
cita9ons?
• Will
allow
a
first
answer
to
the
manifesta9on
of
fact
crea9on
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
26. Revisi9ng
our
Research
Ques9ons:
1. Can
we
find
a
model
for
epistemic
evalua1on
and
knowledge
a5ribu9on
to
describe
all
biological
statements
in
a
straighhorward
way?
– This
seems
to
work
&
agree
with
previous
models
2. If
yes:
can
we
detect
this
evalua9on
–
manually,
– Seems
to
be
the
case,
need
more
annotators
and
automa9cally?
– First
experiments
seem
promising
but
no
conclusions
3. Is
this
model
useful
for
examining
the
mechanism
of
hedging
erosion ?
– Hopefully,
TAC
Corpus
work
will
help
answer
this
ques9on?
Other
corpora?
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
27. In
summary:
• Epistemic
modality
marking
and
knowledge
a5ribu9on:
– are
cri9cal
features
of
scien9fic
text;
– are
manifesta9ons
of
the
objec9fica9on
of
(scien9fic)
subjec9ve
experiences;
– can
be
described
by
our
three-‐part
taxonomy
and
set
of
markers;
– are
instan9ated
largely
through
a
small
set
of
markers,
mostly
prominently
in
matrix
clauses:
‘(deic9c
marker)
+
(repor9ng
verb)
+
that’.
• This
model
can
link
formal
representa9ons
of
biological
statements
to
the
text,
and
improve
knowledge
network
models
with
epistemic
values.
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
28. Acknowledgements
• Thanks
to
NWO
in
the
Netherlands
for
the
ini9al
research
funding
• Thanks
to
Bradley
Allen
at
Elsevier
Labs
for
suppor9ng
my
research
throughout
• Thanks
to
Eduard
Hovy
for
helping
develop
a
model
of
epistemic
modality
as
a
mathema9cal
func9on
• Thanks
to
Lucy
Vanderwende
for
work
on
the
TAC
Corpus
concept
• Thanks
to
Dexter
Pra5
for
work
on
the
BEL
representa9on
• Thanks
to
Agnes
Sandor
for
the
work
on
CKUs
(stay
tuned..)
Introduc9on
|
Methods
and
Results
|
Conclusions
and
Applica9ons
29. References
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Waard,
A.,
Pander
Maat,
H.
(2009).
Categorizing
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2009),
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21-‐23,
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Graeme
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