SlideShare a Scribd company logo
1 of 30
Download to read offline
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	
  
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?	
  	
  
    	
  
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	
  
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	
  
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	
  
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	
  
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	
  
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	
  
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	
  
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	
  
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	
  
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	
  
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	
  
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	
  
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	
  
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	
  
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	
  
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	
  
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	
  
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	
  
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	
  
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	
  
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	
  
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	
  
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	
  
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	
  
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	
  
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	
  
References	
  
•  De	
  Waard,	
  A.,	
  Pander	
  Maat,	
  H.	
  (2009).	
  Categorizing	
  Epistemic	
  Segment	
  Types	
  in	
  
   Biology	
  Research	
  Ar9cles.	
  Wkshp	
  on	
  Linguis9c	
  and	
  Psycholinguis9c	
  Approaches	
  to	
  
   Text	
  Structuring	
  (LPTS	
  2009),	
  September	
  21-­‐23,	
  2009.	
  	
  
•  Feng,	
  Vanessa	
  Wei	
  	
  and	
  Hirst,	
  Graeme	
  (2012).	
  	
  Text-­‐level	
  discourse	
  parsing	
  with	
  rich	
  
   linguis9c	
  features,	
  50th	
  Annual	
  Mee9ng	
  of	
  the	
  Associa9on	
  for	
  Computa9onal	
  
   Linguis9cs	
  (ACL-­‐2012),	
  July,	
  Jeju,	
  Korea	
  
•  Hengeveld,	
  K.	
  &	
  Mackenzie,	
  J.	
  L.	
  (2008),	
  Func9onal	
  Discourse	
  Grammar:	
  A	
  
   Typologically-­‐Based	
  Theory	
  of	
  Language	
  Structure.	
  Oxford	
  Univ.	
  Press,	
  2008.	
  	
  
•  Hyland,	
  K.	
  (2005).	
  Stance	
  and	
  engagement:	
  a	
  model	
  of	
  interac9on	
  in	
  academic	
  
   discourse.	
  Discourse	
  Studies,	
  Vol	
  7(2):	
  173–192.	
  
•  Kim,	
  S-­‐M.	
  Hovy,	
  E.H.	
  (2004).	
  Determining	
  the	
  Sen9ment	
  of	
  Opinions.	
  Proceedings	
  of	
  
   the	
  COLING	
  conference,	
  Geneva,	
  2004.	
  	
  
•  Latour,	
  B.,	
  Woolgar,	
  S.	
  (1979).	
  Laboratory	
  Life:	
  The	
  Social	
  Construc9on	
  of	
  Scien9fic	
  
   Facts.	
  	
  Beverly	
  Hills:	
  Sage	
  Publica9ons.	
  ISBN	
  0-­‐80-­‐390993-­‐4.	
  
•  Light	
  M.,	
  Qiu	
  X.Y.,	
  Srinivasan	
  P.	
  (2004).	
  The	
  language	
  of	
  bioscience:	
  facts,	
  
   specula9ons,	
  and	
  statements	
  in	
  between.	
  BioLINK	
  2004:	
  Linking	
  Biological	
  
   Literature,	
  Ontologies	
  and	
  Databases	
  2004:17-­‐24.	
  
•  Medlock	
  B.,	
  Briscoe	
  T.	
  (2007).	
  Weakly	
  supervised	
  learning	
  for	
  hedge	
  classifica9on	
  in	
  
   scien9fic	
  literature.	
  ACL	
  2007:992-­‐999.	
  
•  Myers,	
  G.	
  (1992).	
  ‘In	
  this	
  paper	
  we	
  report’:	
  Speech	
  acts	
  scien9fic	
  facts,	
  Jnl	
  of	
  
   Pragmatlcs	
  17	
  (1992)	
  295-­‐313	
  
•  Salager-­‐Meyer,	
  F.	
  (1994),	
  Hedges	
  and	
  Textual	
  Communica9ve	
  Func9on	
  in	
  Medical	
  
   English	
  Wri5en	
  Discourse,	
  English	
  for	
  Specific	
  Purposes,	
  Vol.	
  13,	
  No.	
  2,	
  PP.	
  149-­‐170,	
  
   1994.	
  
•  Sándor,	
  Á.	
  and	
  de	
  Waard,	
  A	
  (2012).	
  Iden9fying	
  Claimed	
  Knowledge	
  Updates	
  in	
  
   Biomedical	
  Research	
  Ar9cles,	
  Workshop	
  on	
  Detec9ng	
  Structure	
  in	
  Scholarly	
  
   Discourse	
  at	
  ACL	
  2012	
  (this	
  workshop).	
  	
  
•  Thompson	
  P.,	
  Venturi	
  G.,	
  McNaught	
  J,	
  Montemagni	
  S,	
  Ananiadou	
  S.	
  (2008).	
  
   Categorising	
  modality	
  in	
  biomedical	
  texts..	
  LREC	
  2008:	
  Building	
  and	
  Evalua9ng	
  
   Resources	
  for	
  Biomedical	
  Text	
  Mining	
  2008.	
  
•  Verhagen,	
  A.	
  (2007),	
  Construc9ons	
  of	
  Intersubjec9vity,	
  Oxford	
  University	
  Press,	
  
   2007.	
  
•  Vincze,	
  V.,	
  Szarvas,	
  Farkas,	
  Móra	
  and	
  Csirik,	
  (2008).	
  The	
  BioScope	
  corpus:	
  biomedical
   texts	
  annotated	
  for	
  uncertainty,	
  nega9on	
  and	
  their	
  scopes,	
  BMC	
  Bioinforma9cs	
  
   2008,	
  9	
  (Suppl	
  11):S9.	
  	
  
•  Wilbur	
  W.J.,	
  Rzhetsky	
  A,	
  Shatkay	
  H	
  (2006).	
  New	
  direc9ons	
  in	
  biomedical	
  text	
  
   annota9ons:	
  defini9ons,	
  guidelines	
  and	
  corpus	
  construc9on.	
  BMC	
  Bioinforma9cs	
  
   2006,	
  7:356.	
  

More Related Content

Viewers also liked

Language examples
Language examplesLanguage examples
Language examplescarlyrelf
 
Diglossia
DiglossiaDiglossia
DiglossiaDavid87
 
Putting a ‘naysayer’ in the text: epistemic modality
Putting a ‘naysayer’ in the text: epistemic modalityPutting a ‘naysayer’ in the text: epistemic modality
Putting a ‘naysayer’ in the text: epistemic modalityRon Martinez
 
Sociolinguistic, Varieties of Language, Diglossia
Sociolinguistic, Varieties of Language, DiglossiaSociolinguistic, Varieties of Language, Diglossia
Sociolinguistic, Varieties of Language, DiglossiaElnaz Nasseri
 

Viewers also liked (8)

Language examples
Language examplesLanguage examples
Language examples
 
Diglossia
DiglossiaDiglossia
Diglossia
 
Putting a ‘naysayer’ in the text: epistemic modality
Putting a ‘naysayer’ in the text: epistemic modalityPutting a ‘naysayer’ in the text: epistemic modality
Putting a ‘naysayer’ in the text: epistemic modality
 
Diglossia
Diglossia Diglossia
Diglossia
 
Modality
ModalityModality
Modality
 
Sociolinguistic, Varieties of Language, Diglossia
Sociolinguistic, Varieties of Language, DiglossiaSociolinguistic, Varieties of Language, Diglossia
Sociolinguistic, Varieties of Language, Diglossia
 
Diglossia
DiglossiaDiglossia
Diglossia
 
Sociolinguistics
SociolinguisticsSociolinguistics
Sociolinguistics
 

Similar to A model for epistemic modality and knowledge attribution

Knowledge Representation & Reasoning
Knowledge Representation & ReasoningKnowledge Representation & Reasoning
Knowledge Representation & ReasoningSajid Marwat
 
The Scientific Literature (UG lecture, Feb 2013)
The Scientific Literature (UG lecture, Feb 2013)The Scientific Literature (UG lecture, Feb 2013)
The Scientific Literature (UG lecture, Feb 2013)Stephen Curry
 
Slideshare Presentation of Qualitative Data
Slideshare   Presentation of Qualitative DataSlideshare   Presentation of Qualitative Data
Slideshare Presentation of Qualitative DataDavin Marcus Raja
 
Intro ewt, questionnaires
Intro ewt, questionnairesIntro ewt, questionnaires
Intro ewt, questionnairesleannacatherina
 
CS Artificial Intelligence chapter 4.pptx
CS Artificial Intelligence chapter 4.pptxCS Artificial Intelligence chapter 4.pptx
CS Artificial Intelligence chapter 4.pptxethiouniverse
 
Publishing for the 21st Century: Experiences from the NEUROSCIENCE INFORMATIO...
Publishing for the 21st Century: Experiences from the NEUROSCIENCE INFORMATIO...Publishing for the 21st Century: Experiences from the NEUROSCIENCE INFORMATIO...
Publishing for the 21st Century: Experiences from the NEUROSCIENCE INFORMATIO...Neuroscience Information Framework
 
Parts of research paper
Parts of research paperParts of research paper
Parts of research paperAllanAdem
 
RESEARCH PARADIGMS WORLD VIEWS
RESEARCH PARADIGMS WORLD VIEWSRESEARCH PARADIGMS WORLD VIEWS
RESEARCH PARADIGMS WORLD VIEWSAIMS Education
 
Lean Logic for Lean Times: Varieties of Natural Logic
Lean Logic for Lean Times: Varieties of Natural LogicLean Logic for Lean Times: Varieties of Natural Logic
Lean Logic for Lean Times: Varieties of Natural LogicValeria de Paiva
 
Introduction and Tools of Research
Introduction and Tools of ResearchIntroduction and Tools of Research
Introduction and Tools of ResearchMyke Evans
 
The future of scholarly publishing
The future of scholarly publishingThe future of scholarly publishing
The future of scholarly publishingBjörn Brembs
 
Research methodology the research process
Research  methodology   the research processResearch  methodology   the research process
Research methodology the research processKeethopayan Visvalingam
 
Net coaching &amp; remedial classes p 1 part 2 research
Net coaching &amp; remedial classes p 1 part 2 researchNet coaching &amp; remedial classes p 1 part 2 research
Net coaching &amp; remedial classes p 1 part 2 researchBhumi Dangi
 
How to Ground A Language for Legal Discourse In a Prototypical Perceptual Sem...
How to Ground A Language for Legal Discourse In a Prototypical Perceptual Sem...How to Ground A Language for Legal Discourse In a Prototypical Perceptual Sem...
How to Ground A Language for Legal Discourse In a Prototypical Perceptual Sem...L. Thorne McCarty
 
Is there any a novel best theory for uncertainty?
Is there any a novel best theory for uncertainty?  Is there any a novel best theory for uncertainty?
Is there any a novel best theory for uncertainty? Andino Maseleno
 
質化研究 Study Group 7
質化研究 Study Group 7質化研究 Study Group 7
質化研究 Study Group 7Jenny Chen
 

Similar to A model for epistemic modality and knowledge attribution (20)

Dubrovnik Pres
Dubrovnik PresDubrovnik Pres
Dubrovnik Pres
 
Knowledge Representation & Reasoning
Knowledge Representation & ReasoningKnowledge Representation & Reasoning
Knowledge Representation & Reasoning
 
The Scientific Literature (UG lecture, Feb 2013)
The Scientific Literature (UG lecture, Feb 2013)The Scientific Literature (UG lecture, Feb 2013)
The Scientific Literature (UG lecture, Feb 2013)
 
Scientific writing.pptx
Scientific writing.pptxScientific writing.pptx
Scientific writing.pptx
 
Neo pi
Neo piNeo pi
Neo pi
 
Slideshare Presentation of Qualitative Data
Slideshare   Presentation of Qualitative DataSlideshare   Presentation of Qualitative Data
Slideshare Presentation of Qualitative Data
 
Intro ewt, questionnaires
Intro ewt, questionnairesIntro ewt, questionnaires
Intro ewt, questionnaires
 
CS Artificial Intelligence chapter 4.pptx
CS Artificial Intelligence chapter 4.pptxCS Artificial Intelligence chapter 4.pptx
CS Artificial Intelligence chapter 4.pptx
 
Publishing for the 21st Century: Experiences from the NEUROSCIENCE INFORMATIO...
Publishing for the 21st Century: Experiences from the NEUROSCIENCE INFORMATIO...Publishing for the 21st Century: Experiences from the NEUROSCIENCE INFORMATIO...
Publishing for the 21st Century: Experiences from the NEUROSCIENCE INFORMATIO...
 
Epistemics
EpistemicsEpistemics
Epistemics
 
Parts of research paper
Parts of research paperParts of research paper
Parts of research paper
 
RESEARCH PARADIGMS WORLD VIEWS
RESEARCH PARADIGMS WORLD VIEWSRESEARCH PARADIGMS WORLD VIEWS
RESEARCH PARADIGMS WORLD VIEWS
 
Lean Logic for Lean Times: Varieties of Natural Logic
Lean Logic for Lean Times: Varieties of Natural LogicLean Logic for Lean Times: Varieties of Natural Logic
Lean Logic for Lean Times: Varieties of Natural Logic
 
Introduction and Tools of Research
Introduction and Tools of ResearchIntroduction and Tools of Research
Introduction and Tools of Research
 
The future of scholarly publishing
The future of scholarly publishingThe future of scholarly publishing
The future of scholarly publishing
 
Research methodology the research process
Research  methodology   the research processResearch  methodology   the research process
Research methodology the research process
 
Net coaching &amp; remedial classes p 1 part 2 research
Net coaching &amp; remedial classes p 1 part 2 researchNet coaching &amp; remedial classes p 1 part 2 research
Net coaching &amp; remedial classes p 1 part 2 research
 
How to Ground A Language for Legal Discourse In a Prototypical Perceptual Sem...
How to Ground A Language for Legal Discourse In a Prototypical Perceptual Sem...How to Ground A Language for Legal Discourse In a Prototypical Perceptual Sem...
How to Ground A Language for Legal Discourse In a Prototypical Perceptual Sem...
 
Is there any a novel best theory for uncertainty?
Is there any a novel best theory for uncertainty?  Is there any a novel best theory for uncertainty?
Is there any a novel best theory for uncertainty?
 
質化研究 Study Group 7
質化研究 Study Group 7質化研究 Study Group 7
質化研究 Study Group 7
 

More from Anita de Waard

Mendeley Data: Enhancing Data Discovery, Sharing and Reuse
Mendeley Data: Enhancing Data Discovery, Sharing and ReuseMendeley Data: Enhancing Data Discovery, Sharing and Reuse
Mendeley Data: Enhancing Data Discovery, Sharing and ReuseAnita de Waard
 
Why would a publisher care about open data?
Why would a publisher care about open data?Why would a publisher care about open data?
Why would a publisher care about open data?Anita de Waard
 
Research Object Composer: A Tool for Publishing Complex Data Objects in the C...
Research Object Composer: A Tool for Publishing Complex Data Objects in the C...Research Object Composer: A Tool for Publishing Complex Data Objects in the C...
Research Object Composer: A Tool for Publishing Complex Data Objects in the C...Anita de Waard
 
NFAIS Talk on Enabling FAIR Data
NFAIS Talk on Enabling FAIR DataNFAIS Talk on Enabling FAIR Data
NFAIS Talk on Enabling FAIR DataAnita de Waard
 
CNI 2018: A Research Object Authoring Tool for the Data Commons
CNI 2018: A Research Object Authoring Tool for the Data CommonsCNI 2018: A Research Object Authoring Tool for the Data Commons
CNI 2018: A Research Object Authoring Tool for the Data CommonsAnita de Waard
 
Enabling FAIR Data: TAG B Authoring Guidelines
Enabling FAIR Data: TAG B Authoring GuidelinesEnabling FAIR Data: TAG B Authoring Guidelines
Enabling FAIR Data: TAG B Authoring GuidelinesAnita de Waard
 
Scientific facts are myths, told through fairytales and spread by gossip.
Scientific facts are myths, told through fairytales and spread by gossip.Scientific facts are myths, told through fairytales and spread by gossip.
Scientific facts are myths, told through fairytales and spread by gossip.Anita de Waard
 
Data, Data Everywhere: What's A Publisher to Do?
Data, Data Everywhere: What's  A Publisher to Do?Data, Data Everywhere: What's  A Publisher to Do?
Data, Data Everywhere: What's A Publisher to Do?Anita de Waard
 
Talk on Research Data Management
Talk on Research Data ManagementTalk on Research Data Management
Talk on Research Data ManagementAnita de Waard
 
Networked Science, And Integrating with Dataverse
Networked Science, And Integrating with DataverseNetworked Science, And Integrating with Dataverse
Networked Science, And Integrating with DataverseAnita de Waard
 
Big Data and the Future of Publishing
Big Data and the Future of PublishingBig Data and the Future of Publishing
Big Data and the Future of PublishingAnita de Waard
 
Real-World Data Challenges: Moving Towards Richer Data Ecosystems
Real-World Data Challenges: Moving Towards Richer Data EcosystemsReal-World Data Challenges: Moving Towards Richer Data Ecosystems
Real-World Data Challenges: Moving Towards Richer Data EcosystemsAnita de Waard
 
Data Repositories: Recommendation, Certification and Models for Cost Recovery
Data Repositories: Recommendation, Certification and Models for Cost RecoveryData Repositories: Recommendation, Certification and Models for Cost Recovery
Data Repositories: Recommendation, Certification and Models for Cost RecoveryAnita de Waard
 
The Economics of Data Sharing
The Economics of Data SharingThe Economics of Data Sharing
The Economics of Data SharingAnita de Waard
 
Public Identifiers in Scholarly Publishing
Public Identifiers in Scholarly PublishingPublic Identifiers in Scholarly Publishing
Public Identifiers in Scholarly PublishingAnita de Waard
 
Elsevier‘s RDM Program: Habits of Effective Data and the Bourne Ulitmatum
Elsevier‘s RDM Program: Habits of Effective Data and the Bourne UlitmatumElsevier‘s RDM Program: Habits of Effective Data and the Bourne Ulitmatum
Elsevier‘s RDM Program: Habits of Effective Data and the Bourne UlitmatumAnita de Waard
 
Elsevier‘s RDM Program: Ten Habits of Highly Effective Data
Elsevier‘s RDM Program: Ten Habits of Highly Effective DataElsevier‘s RDM Program: Ten Habits of Highly Effective Data
Elsevier‘s RDM Program: Ten Habits of Highly Effective DataAnita de Waard
 
Charleston Conference 2016
Charleston Conference 2016Charleston Conference 2016
Charleston Conference 2016Anita de Waard
 
The Narrative Structure of Research Articles, or, Why Science is Like a Fairy...
The Narrative Structure of Research Articles, or, Why Science is Like a Fairy...The Narrative Structure of Research Articles, or, Why Science is Like a Fairy...
The Narrative Structure of Research Articles, or, Why Science is Like a Fairy...Anita de Waard
 

More from Anita de Waard (20)

Mendeley Data: Enhancing Data Discovery, Sharing and Reuse
Mendeley Data: Enhancing Data Discovery, Sharing and ReuseMendeley Data: Enhancing Data Discovery, Sharing and Reuse
Mendeley Data: Enhancing Data Discovery, Sharing and Reuse
 
Why would a publisher care about open data?
Why would a publisher care about open data?Why would a publisher care about open data?
Why would a publisher care about open data?
 
Research Object Composer: A Tool for Publishing Complex Data Objects in the C...
Research Object Composer: A Tool for Publishing Complex Data Objects in the C...Research Object Composer: A Tool for Publishing Complex Data Objects in the C...
Research Object Composer: A Tool for Publishing Complex Data Objects in the C...
 
NFAIS Talk on Enabling FAIR Data
NFAIS Talk on Enabling FAIR DataNFAIS Talk on Enabling FAIR Data
NFAIS Talk on Enabling FAIR Data
 
CNI 2018: A Research Object Authoring Tool for the Data Commons
CNI 2018: A Research Object Authoring Tool for the Data CommonsCNI 2018: A Research Object Authoring Tool for the Data Commons
CNI 2018: A Research Object Authoring Tool for the Data Commons
 
Enabling FAIR Data: TAG B Authoring Guidelines
Enabling FAIR Data: TAG B Authoring GuidelinesEnabling FAIR Data: TAG B Authoring Guidelines
Enabling FAIR Data: TAG B Authoring Guidelines
 
Scientific facts are myths, told through fairytales and spread by gossip.
Scientific facts are myths, told through fairytales and spread by gossip.Scientific facts are myths, told through fairytales and spread by gossip.
Scientific facts are myths, told through fairytales and spread by gossip.
 
Data, Data Everywhere: What's A Publisher to Do?
Data, Data Everywhere: What's  A Publisher to Do?Data, Data Everywhere: What's  A Publisher to Do?
Data, Data Everywhere: What's A Publisher to Do?
 
Talk on Research Data Management
Talk on Research Data ManagementTalk on Research Data Management
Talk on Research Data Management
 
History of the future
History of the futureHistory of the future
History of the future
 
Networked Science, And Integrating with Dataverse
Networked Science, And Integrating with DataverseNetworked Science, And Integrating with Dataverse
Networked Science, And Integrating with Dataverse
 
Big Data and the Future of Publishing
Big Data and the Future of PublishingBig Data and the Future of Publishing
Big Data and the Future of Publishing
 
Real-World Data Challenges: Moving Towards Richer Data Ecosystems
Real-World Data Challenges: Moving Towards Richer Data EcosystemsReal-World Data Challenges: Moving Towards Richer Data Ecosystems
Real-World Data Challenges: Moving Towards Richer Data Ecosystems
 
Data Repositories: Recommendation, Certification and Models for Cost Recovery
Data Repositories: Recommendation, Certification and Models for Cost RecoveryData Repositories: Recommendation, Certification and Models for Cost Recovery
Data Repositories: Recommendation, Certification and Models for Cost Recovery
 
The Economics of Data Sharing
The Economics of Data SharingThe Economics of Data Sharing
The Economics of Data Sharing
 
Public Identifiers in Scholarly Publishing
Public Identifiers in Scholarly PublishingPublic Identifiers in Scholarly Publishing
Public Identifiers in Scholarly Publishing
 
Elsevier‘s RDM Program: Habits of Effective Data and the Bourne Ulitmatum
Elsevier‘s RDM Program: Habits of Effective Data and the Bourne UlitmatumElsevier‘s RDM Program: Habits of Effective Data and the Bourne Ulitmatum
Elsevier‘s RDM Program: Habits of Effective Data and the Bourne Ulitmatum
 
Elsevier‘s RDM Program: Ten Habits of Highly Effective Data
Elsevier‘s RDM Program: Ten Habits of Highly Effective DataElsevier‘s RDM Program: Ten Habits of Highly Effective Data
Elsevier‘s RDM Program: Ten Habits of Highly Effective Data
 
Charleston Conference 2016
Charleston Conference 2016Charleston Conference 2016
Charleston Conference 2016
 
The Narrative Structure of Research Articles, or, Why Science is Like a Fairy...
The Narrative Structure of Research Articles, or, Why Science is Like a Fairy...The Narrative Structure of Research Articles, or, Why Science is Like a Fairy...
The Narrative Structure of Research Articles, or, Why Science is Like a Fairy...
 

Recently uploaded

Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking MenDelhi Call girls
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsMark Billinghurst
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking MenDelhi Call girls
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreternaman860154
 
Azure Monitor & Application Insight to monitor Infrastructure & Application
Azure Monitor & Application Insight to monitor Infrastructure & ApplicationAzure Monitor & Application Insight to monitor Infrastructure & Application
Azure Monitor & Application Insight to monitor Infrastructure & ApplicationAndikSusilo4
 
How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?XfilesPro
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptxHampshireHUG
 
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationBeyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationSafe Software
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking MenDelhi Call girls
 
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | DelhiFULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhisoniya singh
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsMemoori
 
Maximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptxMaximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptxOnBoard
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsMaria Levchenko
 
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure servicePooja Nehwal
 
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Alan Dix
 
Key Features Of Token Development (1).pptx
Key  Features Of Token  Development (1).pptxKey  Features Of Token  Development (1).pptx
Key Features Of Token Development (1).pptxLBM Solutions
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)Gabriella Davis
 
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphSIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphNeo4j
 

Recently uploaded (20)

Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR Systems
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
 
Azure Monitor & Application Insight to monitor Infrastructure & Application
Azure Monitor & Application Insight to monitor Infrastructure & ApplicationAzure Monitor & Application Insight to monitor Infrastructure & Application
Azure Monitor & Application Insight to monitor Infrastructure & Application
 
How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
 
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationBeyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
 
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | DelhiFULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial Buildings
 
Maximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptxMaximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptx
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
 
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
 
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
 
Key Features Of Token Development (1).pptx
Key  Features Of Token  Development (1).pptxKey  Features Of Token  Development (1).pptx
Key Features Of Token Development (1).pptx
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphSIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
 

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   •  De  Waard,  A.,  Pander  Maat,  H.  (2009).  Categorizing  Epistemic  Segment  Types  in   Biology  Research  Ar9cles.  Wkshp  on  Linguis9c  and  Psycholinguis9c  Approaches  to   Text  Structuring  (LPTS  2009),  September  21-­‐23,  2009.     •  Feng,  Vanessa  Wei    and  Hirst,  Graeme  (2012).    Text-­‐level  discourse  parsing  with  rich   linguis9c  features,  50th  Annual  Mee9ng  of  the  Associa9on  for  Computa9onal   Linguis9cs  (ACL-­‐2012),  July,  Jeju,  Korea   •  Hengeveld,  K.  &  Mackenzie,  J.  L.  (2008),  Func9onal  Discourse  Grammar:  A   Typologically-­‐Based  Theory  of  Language  Structure.  Oxford  Univ.  Press,  2008.     •  Hyland,  K.  (2005).  Stance  and  engagement:  a  model  of  interac9on  in  academic   discourse.  Discourse  Studies,  Vol  7(2):  173–192.   •  Kim,  S-­‐M.  Hovy,  E.H.  (2004).  Determining  the  Sen9ment  of  Opinions.  Proceedings  of   the  COLING  conference,  Geneva,  2004.     •  Latour,  B.,  Woolgar,  S.  (1979).  Laboratory  Life:  The  Social  Construc9on  of  Scien9fic   Facts.    Beverly  Hills:  Sage  Publica9ons.  ISBN  0-­‐80-­‐390993-­‐4.   •  Light  M.,  Qiu  X.Y.,  Srinivasan  P.  (2004).  The  language  of  bioscience:  facts,   specula9ons,  and  statements  in  between.  BioLINK  2004:  Linking  Biological   Literature,  Ontologies  and  Databases  2004:17-­‐24.   •  Medlock  B.,  Briscoe  T.  (2007).  Weakly  supervised  learning  for  hedge  classifica9on  in   scien9fic  literature.  ACL  2007:992-­‐999.  
  • 30. •  Myers,  G.  (1992).  ‘In  this  paper  we  report’:  Speech  acts  scien9fic  facts,  Jnl  of   Pragmatlcs  17  (1992)  295-­‐313   •  Salager-­‐Meyer,  F.  (1994),  Hedges  and  Textual  Communica9ve  Func9on  in  Medical   English  Wri5en  Discourse,  English  for  Specific  Purposes,  Vol.  13,  No.  2,  PP.  149-­‐170,   1994.   •  Sándor,  Á.  and  de  Waard,  A  (2012).  Iden9fying  Claimed  Knowledge  Updates  in   Biomedical  Research  Ar9cles,  Workshop  on  Detec9ng  Structure  in  Scholarly   Discourse  at  ACL  2012  (this  workshop).     •  Thompson  P.,  Venturi  G.,  McNaught  J,  Montemagni  S,  Ananiadou  S.  (2008).   Categorising  modality  in  biomedical  texts..  LREC  2008:  Building  and  Evalua9ng   Resources  for  Biomedical  Text  Mining  2008.   •  Verhagen,  A.  (2007),  Construc9ons  of  Intersubjec9vity,  Oxford  University  Press,   2007.   •  Vincze,  V.,  Szarvas,  Farkas,  Móra  and  Csirik,  (2008).  The  BioScope  corpus:  biomedical texts  annotated  for  uncertainty,  nega9on  and  their  scopes,  BMC  Bioinforma9cs   2008,  9  (Suppl  11):S9.     •  Wilbur  W.J.,  Rzhetsky  A,  Shatkay  H  (2006).  New  direc9ons  in  biomedical  text   annota9ons:  defini9ons,  guidelines  and  corpus  construc9on.  BMC  Bioinforma9cs   2006,  7:356.