NLP Tools for Biology Literature Mining

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NLP Tools for Biology Literature Mining

• Qiaozhu Mei
• Jing Jiang
• ChengXiang Zhai
• Nov 3, 2004

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What do we have?

• Biology Literature (huge amount of text)
• E.g. Mites in the genus Varroa are the primary
  parasites of honey bees Ten of 22 transfer RNAs
  are in different locations relative to hard
  ticks, and the 12S ribosomal RNA subunit is
  inverted and separated from the 16S rRNA by a
  novel non-coding region, a trait not yet seen in
  other arthropods. (from Biological Abstracts)

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What do we want?

• Named entities
• gene names, protein names, drugs, etc.
• Interaction events between entities
• transcription, translation, post translational
  modification, etc.
• Relationships between basic events
• caused by, inhibited by, etc.
• (from Hirschman et al. 02)

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Preliminary System Structure

Collections of raw textual data
Text Pre-processing NLP
POS Tagger
Parser
Entity Extractor
NPs, VPs, Relations
Genes, proteins, other entities
Nouns, Verbs, etc.
Pre-processed data ready to mine
Text Mining Modules TM

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POS Taggers

• Tree Tagger
• Brill Tagger
• SNoW Tagger
• LT Chunk
• Stanford Tagger

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Results of POS Tagging

• Raw text
• Mites in the genus Varroa are the primary
  parasites of honey bees Ten of 22 transfer RNAs
  are in different locations relative to hard
  ticks, and the 12S ribosomal RNA subunit is
  inverted and separated from the 16S rRNA by a
  novel non-coding region, a trait not yet seen in
  other arthropods.
• (from Biological Abstracts)

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Results of POS Tagging (cont.)
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Results of POS Tagging (cont.)
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Comparison of POS Taggers
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Conclusions

• Existing general-purpose POS taggers work fine
  for our task.
• Most nouns and verbs correctly identified
• There is still room to improve existing POS
  taggers for biology data.
• E.g. to identify gene and protein names
• Speed and adaptability are important.

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A Little Bit More on SNoW

• SNoW has a POS tagger and a shallow parser.
• Speed is reasonable.
• Software is adaptable as help is available from
  CCG.
• The network model can be trained if we have
  training data.

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Result of SNoW Shallow Parser

• NP the 12 S ribosomal RNA subunit VP is ADJP
  inverted and VP separated PP from NP the 16
  S rRNA PP by NP a novel non-coding region
• (from online demo)

• Problems
• Currently the package is not available for
  download from the new CCG page.
• There is still problem running the old package
  on our machine. (compilation, path setting, etc.)

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Parsers

• SNoW (already covered)
• LT-Chunk
• MiniPar
• Collins
• Stanford

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Result of LT-Chunk

• the_DT 12S_JJ ribosomal_JJ RNA_NNP subunit_NN
  (( is_VBZ inverted_VBN and_CC separated_VBN ))
  from_IN the_DT 16S_JJ rRNA_NNP by_IN
  a_DT novel_JJ non-coding_JJ region_NN

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Result of MiniPar

• 16 (the Det 20 det (gov subunit))
• 17 (12S N 20 nn (gov subunit))
• 18 (ribosomal A 20 mod (gov subunit))
• 19 (RNA N 20 nn (gov subunit))
• 20 (subunit N 22 s (gov invert))
• 21 (is be be 22 be (gov invert))
• 22 (inverted invert V E0 i (gov fin))
• E4 (() subunit N 22 obj (gov invert)
• 23 (and U 22 lex-mod (gov invert))
• 24 (separated separate V 22 lex-dep (gov invert))
• 25 (from Prep 22 mod (gov invert))
• 26 (the Det 28 det (gov rRNA))
• 27 (16S N 28 nn (gov rRNA))
• 28 (rRNA N 25 pcomp-n (gov from))

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Results of Collins Parser

• (Sis22 (NPBsubunit55 the/DT 12S/CD
  ribosomal/JJ RNA/NNP subunit/NN ) (VPis21
  is/VBZ (UCPinverted31 (ADJPinverted11
  inverted/JJ ) and/CC (VPseparated31
  separated/VBN (PPfrom21 from/IN (NPBrRNA33
  the/DT 16S/CD rRNA/NN ) ) (PPby21 by/IN
  (NPregion21 (NPBregion44 a/DT novel/JJ
  non-coding/JJ region/NN ,/PUNC, )

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Comparison of Parsers
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Conclusion on Parsers

• MiniPar has advantages so far
• Fast
• Outputs dependency governing info. and useful
  relations
• Provides API
• If SNoW is tuned for the task, we can easily
  plug it into the module.

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Entity Extractors

• Abner extracts protein, DNA, RNA, cell line,
  and cell type
• Yagi extracts only gene names, a brother of
  Abner
• LingPipe Named entity extraction that can be
  trained for different domains.

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Result of Abner

• Ten of ltRNAgt22 transfer RNAslt/RNAgt are in
  different locations relative to hard ticks , and
  the 12 ltproteingtS ribosomal RNA subunitlt/proteingt
  is inverted and separated from the 16 S rRNA by a
  novel non-coding region,

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Result of LingPipe

• Ten of 22 transfer RNAs are in different
  locations relative to hard ticks, and the ltENAMEX
  id"0" type"GENE"gt12S ribosomal RNA
  subunitlt/ENAMEXgt is inverted and separated from
  the ltENAMEX id"1" type"GENE"gt16S rRNAlt/ENAMEXgt
  by a novel non-coding region,

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Comparison of Entity Extractors
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Conclusion on Entities Extractors

• Still a lot of room to improve. However, with
  existing extractors we can begin high level text
  mining work.
• Performances over honeybee data need to be
  evaluated.
• As soon as better extractor is constructed, we
  can plug in easily.

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Summary

• Some Existing NLP tools for supporting Biology
  Literature Mining POS Taggers , Parsers and
  Entity-Extractors are evaluated
• Observations along two lines
• Still considerable room of improvement beyond the
  existing NLP tools, especially customize them for
  special domains.
• We can begin exploring higher-level text mining
  research with support of these toolkits.
• Text Preprocessing Modules are independent, easy
  to plug and play

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References

• Hirschman, L. et al. Accomplishments and
  challenges in literature data mining for biology
  Bioinformatics, 2002
• Dekang Lin. Dependency-based evaluation of
  MiniPar In Workshop on the Evaluation of Parsing
  Systems, 1998

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End of Talk

• Thank you!