A Probabilistic Term Variant Generator for Biomedical Terms

Yoshimasa Tsuruoka and Jun’ichi Tsujii

CREST, JST The University of Tokyo

Outline

Probabilistic Term Variant Generator Generation Algorithm Application: Dictionary expansion

Information extraction from biomedical documents

Recognizing technical terms (e.g. DNA, protein names)

We measured glucocorticoid receptors ( GR )in mononuclear leukocytes ( MNL ) isolated…

Background

Technical Term Recognition

Machine learning based Identifying the regions of terms

⇒ No ID information

Dictionary-based Comparing the strings with each entry in

the dictionary⇒ ID information

Problems of Dictionary-based approaches

Spelling variation degrades recall　⇒ Approximate string searching

False positives degrade precision　⇒ Filtering by machine learning

Exact String Searching

Example Text Phorbol myristate acetate induced Egr-1

mRNA…

DictionaryEGPEGR-1EGR-1 binding protein

: ⇒　 Any of them does not

match

Edit Distance

Defines the distance of two strings by the sequence of three kinds of operations. Substitution Insertion Deletion

Ex.) 　　 board 　→　 abord Cost = 2 (delete `a’ and add `a’)

Automatic Generation of Spelling Variants

Variant Generator

NF-Kappa B (1.0)NF Kappa B (0.9)NF kappa B(0.6)NF kappaB (0.5)NFkappaB (0.3)

:

GeneratorNF-Kappa B

Each generated variant is associated with its generation probability

Generation Algorithm

T cell (1.0)

T-cell (0.5) T cells (0.2)

T-cells (0.1)

0.5

0.2

0.2

Recursive generation　 P = P’ x Pop

Collecting Examples of Spelling Variation

Abbreviation Extraction （ Schwartz 2003 ） Extracts short and long form pairs

Short form Long form

AA Alcoholic Anonymous

American

Americans

Arachidonic acid

arachidonic acid

anaemia

anemia

:

Learning Operation Rules

Operations for generating variants Substitution Deletion Insertion

Context Character-level context: preceding (following)

two characters Operation Probability

contextf

operationcontextfcontextoperationP

,

Probabilistic Rules

Probability

Left-context

TargetRight-

contextOperation

0.96 * ‘ End of String

Delete

0.96 Start of String

I m Replace ‘I’ with ‘i’

0.95 * H yd Replace ‘H’ with ‘h’

: : : : :

0.75 phEnd of String

Insert ‘y’

: : : : :

Example (1)

Generation Probability

Generated Variants Frequency

1.0 (Input) NF-kappa B 857

0.417 NF-kappaB 692

0.417 nF-kappa B 0

0.337 Nf-kappa B 0

0.275 NF kappa B 25

0.226 NF-kappa b 0

: : :

Example (2)

Generation Probability

Generated Variants Frequency

1.0 (input) antiinflammatory effect 7

0.462 anti-inflammatory effect 33

0.393 antiinflammatory effects 6

0.356 Antiinflammatory effect 0

0.286 antiinflammatory-effect 0

0.181 anti-inflammatory effects 23

: : :

Example (3)

Generation Probabilitiy

Generated Variants Frequency

1.0 (Input) tumour necrosis factor alpha 15

0.492 tumor necrosis factor alpha 126

0.356 tumour necrosis factor-alpha 30

0.235 Tumour necrosis factor alpha 2

0.175 tumor necrosis factor alpha 182

0.115 Tumor necrosis factor alpha 8

: : :

Application:Dictionary Expansion

Expanding each entry in the dictionary Threshold of Generation Probability: 0.1 Max number of variants for each entry: 20

Protein Name Recognition

Information Extraction Longest match GENIA corpus

Results of Dictionary Expansion

a

Conclusion

Probabilistic Variant Generator Learning from actual examples Dictionary expansion by the generator

improves recall without the loss of precision.