Sub-language Processing for phenotype curation

Hong CuiUniversity of Arizona

Phenotype RCN Feb 25-27, 2013

SUB-LANGUAGE PROCESSING FOR PHENOTYPE CURATION

Agenda• CharaParser• Methodology• Evaluation• Applications• CharaParser for Phenoscape• New modules• Evaluations• Challenges

“Fine-Grained Semantic Mark-up”• To annotate factual information from textual morphological descriptions of biodiversity in such a detailed manner that the machine readable annotation itself provides information equivalent to the original text.

An Example

Previous Research• Syntactic parsing approach (Taylor, 1995 ; Abascal & Sanchenz, 1999; Vanel, 2004)• Interactive extraction (Diederich, J., Fortuner, R. & Milton, J. 1999).• Semi-supervised bootstrapping for lexicons (Ellen Riloff, 1999) • Supervised regular expression rule learning (Soderland, 1999; Tang & Heidorn 2008)•Ontology driven and parallel text (Woods et. al. 2004)• Supervised association rule learning (Cui & Heidorn, 2007)

General-Purpose Parsers?

CharaParser Approach1. Unsupervised machine learning to find anatomy and

character terms from descriptions automatically• No need to prepare training examples• 50% - 80% terms learned

2. General-purpose syntactic parser (e.g., Stanford Parser) to parse syntactic structure of sentences• No need to create special-purpose, domain-dependent

parser• Learned lexicon from 1 is used to adapt the Parser for

biodiversity domains3. Intuitive rules to produce annotations from parse

trees.

Unsupervised lexicon learning

If it is known “roots” is an organ:

•Roots yellow to medium brown or black, thin.• Petals yellow or white• Petals absent;• Subtending bracts absent;• Abaxial hastula absent;

CharaParser: Term Reviewer

Ontology Term Organizer

Compared against a Heuristics-Based Method

• Parser performance evaluated on the same data sets.• CharaParser: unsupervised learning + Stanford Parser• Heuristics-based: unsupervised learning + regular expression rules

Annotation Problems• Chunk errors:• Leaves oblanceolate to lanceolate, largest 14–20(–40) × 3–

4(–5) mm, pliant;• Attachment errors:• on outer cypselae, crowns of bristlelike scales ca. 0.5 mm;

on inner, of dusky white or pale yellow, plumose bristles 5–6 mm.

• Semantics:• straight posterolateral bounding ridges to subtriangular ,

bilobed ventral muscle field;

Applications at Various Development Stages• Convert XML markup to • SDD for identification key generation• Character matrices for tree of life• RDF for the Semantic Web and search • Use marked-up descriptions to support search• FNA Experimental Search • Data source is RDF triples• Allow character based search• Plants that give yellow flowers at 200-400 meter elevation in April in North

Carolina

To-Dos• Tighter integration of ontologies in annotation process.• Currently internal glossaries are used in place of

ontologies to link a character state (e.g., “red”) to a character (“color”)• Synonyms are not controlled• “Petiolate” = “with petiole”

• Continue to reduce annotation errors• Accommodate various syntactic styles • Diagnosis paragraphs• Comparison among different taxa

Phenotype Curation• Convert character and character state information from natural language descriptions to EQ statements

Curator Mental Process

readdescription

Identify key phrases (raw EQ)

ontologized EQ

ontologies

Adapted CharaParserCharacter Description

State Descriptions

CharaParser

XML to Raw EQs

Raw EQs to Final

EQs

Ontologies

Evaluations• Internal evaluation: • The development corpus (three publications on fishes and archosaurs)

provided 1,200 character descriptions. 100 of them included in the internal evaluation benchmark.• Raw EQ performance: 90%• Final EQ performance: 50% • BioCreative2012 evaluation:• 50 descriptions independently selected by the organizer (>50% Qs

were not in ontologies)• Gold standard created by chief phenoscape curator (raw and final)• Three biocurators worked in two modes (Phenex vs.

Phenex+CharaParser)• Raw EQ performance: CharaParser better than biocurators• Final EQ perfoamnce: biocuration better than CharaParser • Inter-curator agreements:

Inter-Curator AgreementsPrecision Recall

Curator 1 vs 2 39 49Curator 1 vs 3 47 56Curator 2 vs 3 77 71

Error Analyses• Various fixable syntactic problems• E.g., “digits I-III”

• Curation granularity• CharaParser generated more candidate EQs than curators• “Preopercular latero-sensory canal leaves preopercle at first

exit and enters a plate: yes/no”• Annotating relations (relational quality)• “contact between …”

Ontology Access• Currently use keyword-based search• Class labels and exact, narrower, and related synonyms• False positives • acute(shape) =? acute (process)•  "margin" is a broad synonym of "marginal zone of embryo" in

UBERON• Pre-composed terms in ontology• “ceratobranchial 5 tooth”, “rib of vertebra 5”, “body of

humerus”• Ambiguious term use in descriptions

• ‘epibranchial 1’ => epibranchial 1 element? bone? cartilage?• No matching

Exploration of Solutions• Experimented with• Word sense disambiguation: • “crinkly” not in PATO• Candidate matches: [undulate->1.00000000000002]

[obovate->1.00000000000001] [flat->1.00000000000001] [flattened->1] [circinate->0.884697579551583]

• Experimenting with• Subsets• Specify included classes: e.g. classes related to vertebrates• Specify excluded classes: e.g. exclude certain developmental stages

• Ideas to try out: • Bootstrapping to narrow down the search space• starting from known classes• evaluating candidate matches based on the distances to the known classes

and other source of evidences.

Annotation consistency• Instructions given to human curators are helpful to CharaParser • Restricted relation list:• http://phenoscape.org/wiki/Guide_to_Character_Annotation#Relations_used_for_post-compositions

Feed more info to EQ generation module

Character Description

State DescriptionsCharaPars

er

XML to EQs

Raw EQs to Final EQs

Ontologies

Recent Improvements• Explorer of Taxon Concepts project• Making it a pure-java program/web-based application• Currently requires MySQL + Perl• Making it faster• Optimization of the program• Removing MySQL and reducing I/O• “Parallel” computing using java threads

• Preliminary evaluation shows • 20 times faster: 2 sec/taxon description• Memory requirements increased by 3 folds

Acknowledgements• Fine-Grained Semantic Markup Project (current and past)• James Macklin: Agriculture and Agri-Food Canada • Robert (Bob) Morris, Alex Dusenbery: UMass-Boston• Hariharan Gopalakrishnan, Zilong Chang, Thomas Rodenhausen, Mohan

Krishna Gowda, ParthaPartha Pratim Sanyal, Chunshui Yu: University of Arizona

• Phenoscape Project• Chris Mungall: Laurence Berkeley National Lab• Melissa Haendel : Oregon Health & Science University • Paula Mabee, Alex Dececchi: University of South Dakota• Jim Balhoff, Wasila Dahdul, Hilmar Lapp, Todd Vision: NESCent

• NSF ABI and EF Programs• The Flora of North American Project