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Using ontologies for text processing. Overview. Thesis: Ontologies (or even more elaborated knowledge-bases) are required to solve the lexical ambiguity problem Describe the lexical ambiguity problem and its central importance in natural language processing - PowerPoint PPT Presentation
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Lawrence Hunter & K. Bretonnel Cohen Center for Computational PharmacologyUCHSC School of Medicine
http://[email protected]
Using ontologies for text processing
Overview
Thesis: Ontologies (or even more elaborated knowledge-bases) are required to solve the lexical ambiguity problemDescribe the lexical ambiguity problem and its central importance in natural language processingDemonstrate how GO, combined with Direct Memory Access Parsing, provides a simple solution to some instances of this problemArgue no alternative is likely to work as well
Lexical Ambiguity
A word (character string) means different things in different contexts – How can a program disambiguate (tell which is meant)?
Widespread problem even in “simple” bioNLP– DNA vs. mRNA vs. protein [Hatzivassiloglou et al. 2001]– Gene symbol vs. non-gene acronym [Pustejovsky et al.
2001], [Chang et al. 2002], [Liu and Friedman 2003], [Schwartz and Hearst 2003]
– Gene/product vs. any other noun [Tanabe and Wilbur, 2002]
A particular example
“Hunk” can be a– Cell type: human natural killer– Gene: hormonally upregulated Neu-associated kinase– Medical abbreviation: radiographic/orthopedic joint
classification system– Non-technical English: a large lump, piece, or portion
All occur in Medline documents….(e.g. “hunk of metal” in article on ambulance design)
How do ontologies help?
The idea that knowledge is relevant to understanding words in context is controversial only among linguists, but…
Direct Memory Access Parsing (DMAP) [Martin, 1991] [Fitzgerald, 2000] technique demonstrates the power of knowledge-based method for disambiguation
GO & similar efforts make DMAP (or other knowledge-based methods) practical today
What is DMAP?
Conceptual parser– Maps from text to conceptual representations organized in
packaging and abstraction hierarchies (like GO)– In contrast to: pure syntactic parsers, pattern matching and
machine learning systems
Conceptual representations include lexical patterns that specify how to recognize the concept in text– Patterns consist of text literals and/or references to other concepts– Organized around concepts, not words; no independent lexicon.
Recognition creates expectations for related concepts
A real example
ID: cell-type-HUNKIS-A: cell-typelex: human natural killer
HUNK
RESULTS
ID: gene-26559IS-A: genelex:
hormonally upregulated Neu-associated kinase
HUNK
hormonally upregulated neu tumor-associated kinase
ID: GO-0006350lex: transcription expression
ID: gene-expressionslots: expressed-item: gene mechanism: expressionlex: (gene) (expression)
“…Hunk expression is restricted to subsets of cells…” [Gardner et al. 2000]
(parse ‘(Hunk))e-gene-26559 begin: 1 end: 1e-cell-type-HUNK begin: 1 end: 1
(parse ‘(Hunk expression))c-gene-expression-1 begin: 1 end: 2 expressed-item: e-gene-26559 begin: 1 end: 1 mechanism: GO:0006350 begin: 2 end: 2
DMAP output with and without context
Hunk alone: ambiguous
Hunk expression:not ambiguous
DMAP can handle much more complex constructions
“Hunk is expressed in mouse epithelial cells during cell proliferation.”
c-localized-gene-expression
expressed-item: e-gene-26559
mechanism: GO:0006350
where: c-epithelial-cell
taxon: ncbi_10090
when: GO:0008283
But uses our enriched knowledge-base, not just GO
Even just DMAP/GO is a big win
Recall 7,042 ambiguous symbols for 9,723 genes
Straightforward to disambiguate symbols that map to 2 or more genes when:– Each ambiguous gene referent has GO annotations, and – There is no overlap between the annotations for the genes
3,333 of the symbols (for 4715 of the genes) have this feature – nearly half the problem is solved!
Compare the alternatives
Statistical or machine learning approaches– Must avoid being fooled by word “cells” in example– Scalability: need statistics for many covariates of every
ambiguous word; doesn’t exploit the abstraction hierarchy
Full syntactic parse doesn’t disambiguate at all!
Cascaded FST’s, pattern-matching, etc.– Where is source of knowledge for these?– Much DMAP lexical information can be taken directly from
GO (and LocusLink, etc.)
Acknowledgments
Philip V. Ogren
Daniel J. McGoldrick
Christoffer S. Crosby
Jens Eberlein
George K. Acquaah-Mensah
I/NET’s (http://inetmi.com) CM / CMP software
Support from Wyeth Genetics Institute, NIAAA
http://compbio.uchsc.edu
Biognosticopoea representation of the hunk gene
Attachment ambiguity
Attachment ambiguity– These findings suggest that FAK functions in the
regulation of cell migration and cell proliferation. (Gilmore and Romer 1996:1209)
– What does FAK do?• ALMOST RIGHT:• FAK functions in the regulation of cell migration• FAK functions in cell proliferation• RIGHT:• FAK functions in the regulation of cell migration• FAK functions in the regulation of cell proliferation
Attachment ambiguity
GO-0016477 isA go-process lex: cell migrationGO-0008283 isA go-process lex: cell proliferationGO-0042127 isA go-process lex: regulation of cell proliferation regulation of ((go-process) and)* cell proliferationGO-0030334 lex: regulation of cell migration regulation of ((go-process) and)* cell migration
Attachment ambiguity
(parse ‘(These findings suggest that FAK functions in the regulation of cell migration and cell proliferation))
GO:30334
begin: 9 end: 12
GO:0042127
begin: 9 end: 15
What do we have so far?
Gene Ontology
UMLS
MeSH
…
What more do we need?
FamilyLocation– Macroanatomical– Subcellular localization
StructureFunction– Disease associations– Protein/protein interactions– …..
Where can we get it?
GO definitions
UMLS definitions
MeSH notes
Biomedical literature
If you don’t like DMAP….
full syntactic parse first
cascaded FST’s
“a little syntax, a little semantics”
machine learning
pattern-matching
All can benefit from ontology/KB