OntoGene in the BioNLP Shared Task and in …nactem.ac.uk/talk_slides/Rinaldi.pdfOutline Background...

OntoGene in the BioNLP Shared Task

and in BioCreative II.5

Fabio Rinaldi

Outline

Background our work on GENIA participation to BioCreative II

The IntAct activity Interactors (AIME 09), Methods (SMBM08), Organisms

(BioNLP 09), Interactions (CICLING 09)

Recent work: BioNLP shared task BioCreative II.5

OntoGene: the beginnings

BioNLP identified as a 'hot' area for research Leverage on the work done on terminology

structuring original focus: ontology learning later refocused on ontology usage

Gradually moved into relation extraction leverage upon dependency structures (Pro3Gres) organize different tools into an NLP pipeline

How does it work?

The pipeline delivers: tokens with unique identifiers terms and their heads chunks and their heads dependency relations; encoded as (sentenceid, type,

head, dependent); can be delivered either as CSV or XML

OGRM application makes use of this information (stored in a Prolog database) in order to extract domain relations by means of cascading rules

NLP Pipeline sentence splitting (mxterminator), tokenizer

(Penn Treebank tokenizer), POStagger (MXPOST), lemmatizer (morpha), NG/VG chunker (LTCHUNK), dependency parser (Pro3Gres)

each tool has a wrapper to make inputoutput XMLbased

other outputs are possible: CSV, Prolog

integrates LingPipe, Term Detection...

support various postprocessing of dependency relations

Performance: 1 hour to parse the GENIA corpus

dual core AMD opteron 2.5 Ghz, 8GB ram 45 min for parsing

Pro3Gres parse example

OG-RM: cascading rules

X1

By [by, through, via]

X3

X2

subj

prep

pobj

A B

H

agent target

A regulates BB is regulated by Athe regulation of B by A

semRel(xrel([H,A,B]), direct_transitive([H,A,B])).semRel(xrel([H,A,B]), passive([H,B,A])).semRel(xrel([H,A,B]), nominalization([H,B,A])).

OG-RM: cascading rules

X1

By [by, through, via]

X3

X2

subj

prep

pobj

A B

H

agent target

A H [nominalisation]

trigger

BPrep [of, ..]

xrel

deprel

A triggers the H of B

References

Fabio Rinaldi, Gerold Schneider, Kaarel Kaljurand, Michael Hess, Martin Romacker. An environment for relation mining over richly annotated corpora: the case of GENIA. BMC Bioinformatics 2006, 7(Suppl 3):S3. doi:10.1186/147121057S3S3

Outline





Krallinger et al., Overview of the proteinprotein interaction annotation task of BioCreative II,

Genome Biology (2008), vol. 9, suppl. 2, pp. S4

IPS: Our Approach

Protein Name Detection and Disambiguation identification of proteins organismbased disambiguation further disambiguation

Interaction Detection generation of potential interactions filtering of candidate interactions

Syntaxbased filter Novelty filter

Evaluation

UniProt

Annotated Abstract

IMS

Detection of experimental methods, based on PSIMI taxonomy

Best official results !!!!!

References

Fabio Rinaldi, Thomas Kappeler, Kaarel Kaljurand, Gerold Schneider, Manfred Klenner, Simon Clematide, Michael Hess, JeanMarc von Allmen, Pierre Parisot, Martin Romacker, Therese Vachon. OntoGene in BioCreative II. Genome Biology, 2008, 9:S13.

Outline


The IntAct activity Methods (SMBM08), Interactors (AIME 09), Organisms



Detection of Biological Interactions

from Biomedical Literature[SNF 100014 / 118396]

Duration: 18 months (April 2008 – September 2009) SNF Funding: 114'046 CHF Novartis Funding: ~ 70'000 CHF University funding: 50% Fabio's position

IntAct

Can be used as source of interactions, interactors, methods, organisms, “snippets” Used to derive distributional frequencies Used to derive a gold standard for testing purposes (for

IMS and TX): 621 PubMedindexed articles

Subtasks: IMS: Experimental Methods TX: Organism Detection PID: Protein Identification and Disambiguation PPI: Protein Interactions

Balance so far

Highlights: IntAct, BioNLP shared task, BioCreative Publications:

Genome Biology paper finally published 4 poster presentations (G2S, LREC, CICLING, ISWC) 4 conference papers (SMBM, OWLED, CICLING,

AIME) 2 workshop presentations [BioNLP workshop & shared

task]

Invited presentations: FBK, Trento; DBTA, Basel; CCP, Denver.

Outline





Introduction

Approach originally developed for participation in the BioCreative proteinprotein interaction task

Used also on an internal project based on the IntAct dataset of protein interactions

Adaptation to the BioNLP shared task took approximately one month

Based on straightforward rewriting of syntactic structures to event structures, taking statistics from training data into account

Preprocessing

Lingpipe for sentence splitting, tokenization, and PoS tagging (GENIA training model)

Term annotation: only terms provided in a1,a2 files (in 10 cases not compatible with tokenization)

Lemmatization (morpha) used only by dep. Parser Chunking using LTCHUNK & detecting chunk heads Dependency parsing with Pro3Gres, only among

chunks

Data format

Tokens (tokID > lemma, Pos Tag, offset) Chunks (tokID > chunk, chunk type, head) Terms (tokID > term ID) Sentences (sent ID > tokens Ids) Dependences (dependent ID > head ID)

Data from training

word_to_freq(+Word, F) eword_to_event(+EventWord, EventType,

EventArgs, F1, F2) F1: frequency of EventWord, EventType, EventArgs F2: frequency of EventWord as trigger

Domination path Direct domination

“regulates expression” Chunk internal domination

“inducible Oct2 expression”

Trigger generation

Event structure generation

Event argument filling

BioNLP shared task

Outline





Our approach

Core pipeline delivers rich annotation format Used to process training and test data

Entities Detection and Disambiguation (IntAct approach) [Orgbased disambiguation]

Candidate interactions Initial training based on GENIA (IntAct approach) Statistics adjusted using training data

Results: “impressively good” AUC training: ~ 22%

BioCreative II.5

Acknowledgments Kaarel Kaljurand Gerold Schneider Thomas Kappeler Simon Clematide

Therese Vachon Martin Romacker Josef Scheiber

www.ontogene.org

OntoGene in the BioNLP Shared Task and in …nactem.ac.uk/talk_slides/Rinaldi.pdfOutline Background...

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