Biological information extraction from natural language text

1
Biological information extraction from natural
language text

• Chitta Baral
• Arizona State University

2
Goal

• Extract simple information from text.
• This is somewhat simpler than complete natural
  language understanding
• Examples of simple information (structure is
  anticipated)
• John was in Phoenix in March
• at( John, Phoenix, March)
• Protein-x in presence of enzyme y breaks down to
  components z and w.
• breaks_in_presence_of( x, y, z , w )
• Not so simple information (meta-informations,
  unanticipated or untargeted structure)
• John only visits cities where he has a friend

3
Main approach

• Use extraction rules that can extract the
  targeted information
• Extract P(X,Y,Z) from a sentence if in that
  sentence X is a proper noun, Y is a verb that
  immediately follows the noun and Z is a noun
  phrase that immediately follows Y.
• Coming up with extraction rules
• Manually
• Learning extraction rules
• Develop your own learning program
• Cast your problem appropriately so as to use
  existing learning programs (such as Progol, FOIL,
  etc.)
• Take an existing information extraction system
  and make appropriate changes to it so as to make
  it applicable for our case

4
Learning extraction rules

• Mark the text of what is to be extracted
• Parse the text (with markings) and do part of
  speech tagging
• Extract pattern
• Use the pattern on other text, and add conditions
  or modify pattern to avoid false positives.
• Repeat the above steps until an acceptable
  performance is achieved.

5
An example

• HMBA could inhibit the MEC-1 cell proliferation
  by down-regulation of PCNA expression, it could
  also induce apoptosis effectively that might be
  through the way of up-regulation of bax and bcl-2
  gene expression.
• Interaction(HMBA, inhibit, MEC-1 cell
  proliferation)
• Interaction(HMBA, down-regulation, PCNA
  expression)

6
Parsing and POS tagging

• word(tag 'NNP' ,arg(1),'HMBA'),
• vg(word(tag 'MD','could'),
• word(tag 'VB'
  ,arg(2),'inhibit')),
• ng(arg(3), word(tag 'DT','the'),
• word(tag
  'NNP','MEC-1'),
• word(tag 'NN','cell'),
• word(tag
  'NN','proliferation')
• ),
• word(tag 'IN','by'),
• word(tag 'NN','down-regulation'),
• word(tag 'IN','of'),
• ng(word(tag 'NNP','PCNA'),
• word(tag 'NN','expression')
• ),
• word(tag ',',','),
• word(tag 'PRP','it'),
• vg(word(tag 'MD','could'),
• word(tag 'RB','also'),

• word(tag 'NN','apoptosis'),
• word(tag 'RB','effectively'),
• word(tag 'WDT','that'),
• vg(word(tag 'MD','might'),
• word(tag 'VB','be')),
• word(tag 'IN','through'),
• ng(word(tag 'DT','the'),
• word(tag 'NN','way')
• ),
• word(tag 'IN','of'),
• word(tag 'NN','up-regulation'),
• word(tag 'IN','of'),
• word(tag 'NN','bax'),
• word(tag 'CC','and'),
• ng(word(tag 'JJ', 'bcl-2'),
• word(tag 'NN','gene'),
• word(tag 'NN','expression')
• )

7
An alternate way to code

• sentence(s).
• first(s, p1).
• next(p1,p2). next(p2,p3). next(p3,p4).
  next(p4,p5).
• next(p5,p6). next(p6,p7). next(p7,p8).
  next(p8,p9).
• next(p9,p10). next(p10,p11). next(p11,p12).
  next(p12,p13).
• next(p13,p14). next(p14,p15). next(p15,p16).
  next(p16,p17).
• next(p17,p18). next(p18,p19). next(p19,p20).
  next(p20,empty).
• type(p1, word). tag(p1, nnp). content(p1, hmba).
  marked(p1,arg1).
• type(p2, vg).

8
POS tags

• NNP proper noun
• MD -- modal
• VB verb base form
• DT -- determiner
• NN common noun
• IN -- preposition
• PRP
• RB -- adverb
• WDT --
• CC coordinating conjunction
• JJ -- adjective

9
Extracted interaction rule

• extract( word(tag NNP,_h18724),
• word(tag VB,_h18725),
• ng(_h18726)
• ,
• interact(_h18724,_h18725,_h18726),
• true).

10
Tagged text

• Interact (HMBA,
• word (tag MD, could),
• word (tag VB, inhibit),
• word (tag DT, the),
• word (tag NNP,MEC-1),
• word (tag NN, cell),
• word (tag NN,
  proliferation)).
• Interact (HMBA, down-regulation,
• word (tag NNP,PCNA),
• word (tag NN,
  expression)).

11
Prolog code for learning extraction rules

• -import append/3 from basics.
• learn( S)- find_interact( S,I,P), nl, write( I),
  nl, write( P), write_file( P,I).
• P extraction pattern
• I interaction fact
• S tagged text
• find_interact(word(T,arg(1),_) R, interact
  (A,B,C), P ) -
• AX, pattern ( word (T,A)PR,P),
• find_interact (SR, interact
  (A,B,C),PR).
• More rules for find_interact.
• pattern( W,P)- PW.
• write_file( P,I)- Eextract (P, I, true), open(
  'extract.P', append, F),
  write( F, E), write( F,'.'), nl( F), close(
  F).

12
A set of extraction patterns

• extract( word (tag 'NNP',_h13664),word(tag
  'VB',_h13665),
• word (tag
  'NNP',_h13666),interact(_h13664,_h13665,_h13666)
  ,true).
• extract( word (tag 'NNP',_h62915),vg(_h62916),
  ng(_h62917),
• interact(_h62915,_h62916,_h6291
  7),true).
• extract( word (tag 'NNP',_h112469), word
  (tag 'NN',_h112470),
• ng(_h112471),
  interact(_h112469,_h112470,_h112471),true).
• extract( word (tag 'NNP',_h161953),word(tag
  'NN',_h161954),
• word (tag
  'NNP',_h161955),
• interact(_h161953,_h161954,_h16
  1955),true).
• extract( word (tag 'VB',_h17857),vg(_h17858)
  ,ng(_h17859),
• interact(_h17857,_h17858,_h1785
  9),true).
• extract( word (tag 'NNP',_h42739),word(tag
  'NN',_h42740),ng(_h42741),
• interact(_h42739,_h42740,_h4274
  1),true).
• extract( word (tag 'NNP',_h44071),word(tag
  'NN',_h44072),ng(_h44073),
• interact(_h44071,_h44072,_h4407
  3),true).
• extract( word (tag 'NNP',_h16431),word(tag
  'NN',_h16432),ng(_h16433),
• interact(_h16431,_h16432,_h1643
  3),true).

13
Code that extracts patterns

• - load_dyn( 'extract.P').
• matcher(_,,_).
• matcher( SHST,SHPT,_) - matcher(ST,PT,_).
• matcher( SHST,PHPT,_) - SH \ PH,
• 
  matcher( ST,PHPT,_).
• run( S)- process( S).
• process(S) - extract( P,F,_), matcher( S,P,_),
  write_file(F), fail.
• process(_).
• write_file(I)- open( 'interact.P', append,File),
  write(File,I), write(File,'.'),nl(File),
  close(File).

14
Applications of interest

• Finding interaction between genes and proteins
• Given a set of genes, say obtained using micro
  array experiments, using such extracted
  information get a rough idea about the various
  genes and proteins that interact with these
  genes.
• Now build a pathway.