Download Information extraction - Arizona State University

Biological information extraction
from natural language text
Chitta Baral
Arizona State University
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
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
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.
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 upregulation of bax and bcl-2 gene expression.
• Interaction(HMBA, inhibit, MEC-1 cell
proliferation)
• Interaction(HMBA, down-regulation, PCNA
expression)
Parsing and POS tagging
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[
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= 'VB'],'induce')
]),
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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')
])
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]
An alternate way to code
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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). …
POS tags
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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
Extracted interaction rule
• extract( [ word([tag = NNP],_h18724),
word([tag = VB],_h18725),
ng(_h18726)
],
interact(_h18724,_h18725,_h18726),
true).
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)]).
Prolog code for learning extraction
rules
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:-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
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find_interact([word([T,arg(1)],_) | R], interact (A,B,C), P ) :A=X, pattern ([ word ([T],A)|PR],P),
find_interact (SR, interact (A,B,C),PR).
More rules for find_interact.
pattern( W,P):- P=W.
write_file( P,I):- E=extract (P, I, true), open( 'extract.P', append, F),
write( F, E), write( F,'.'), nl( F), close( F).
A set of extraction patterns
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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,_h62917),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,_h161955),true).
extract( [word ([tag = 'VB'],_h17857),vg(_h17858),ng(_h17859)],
interact(_h17857,_h17858,_h17859),true).
extract( [word ([tag = 'NNP'],_h42739),word([tag = 'NN'],_h42740),ng(_h42741)],
interact(_h42739,_h42740,_h42741),true).
extract( [word ([tag = 'NNP'],_h44071),word([tag = 'NN'],_h44072),ng(_h44073)],
interact(_h44071,_h44072,_h44073),true).
extract( [word ([tag = 'NNP'],_h16431),word([tag = 'NN'],_h16432),ng(_h16433)],
interact(_h16431,_h16432,_h16433),true).
Code that extracts patterns
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:- load_dyn( 'extract.P').
matcher(_,[],_).
matcher( [SH|ST],[SH|PT],_) :- matcher(ST,PT,_).
matcher( [SH|ST],[PH|PT],_) :- SH \== PH,
matcher( ST,[PH|PT],_).
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).
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.