Text mining tools for semantically enriching scientific literature

Loading...

Flash Player 9 (or above) is needed to view presentations.
We have detected that you do not have it on your computer. To install it, go here.

0 comments

Post a comment

    Post a comment
    Embed Video
    Edit your comment Cancel

    10 Favorites

    Text mining tools for semantically enriching scientific literature - Presentation Transcript

    1. Text mining tools for semantically enriching the scientific literature Sophia Ananiadou Director National Centre for Text Mining School of Computer Science University of Manchester
    2. Need for enriching the literature • Need for semantic search i.e. beyond keywords • Need for technologies enabling focused semantic search via the creation of semantic metadata from literature “The current scientific literature, were it to be presented in semantically accessible form, contains huge amounts of undiscovered science” Peter Murray-Rust, Data-driven science: A Scientist’s view. NSF/JISC Repositories Workshop, 2007
    3. Impact of text mining • Extraction of named entities (genes, proteins, metabolites, etc) • Discovery of concepts allows semantic annotation of documents – Improves information access by going beyond index terms, enabling semantic querying – Improves clustering, classification of documents – Visualisation based on semantic metadata derived from text mining results
    4. Beyond named entities: facts • Extraction of relationships, events (facts) for knowledge discovery – Information extraction, more sophisticated annotation of texts (fact annotation) – Enables even more advanced semantic querying
    5. Enriched annotation • Text Mining provides enriched annotation layers – the user will be able to carry out an easily expressed semantic query which will deliver facts matching that semantic query rather than just sets of documents he has to read… • Information Extraction and not just Information Retrieval • Fact extraction and not just sentence extraction
    6. Annotations derived from Text Mining lexicon ontology text processing raw deep annotated part-of-speech named entity (unstructured) syntactic (structured) tagging recognition text parsing text ……………………….... S ... Secretion of TNF was abolished by BHA in VP PMA-stimulated U937 NP VP cells. …………………… PP NP PP PP NP NN IN NN VBZ VBN IN NN IN JJ NN NNS . Multi-layered Secretion of TNF was abolished by BHA in PMA-stimulated U937 cells . annotations protein_molecule organic_compound cell_line negative regulation
    7. Mining associations from MEDLINE • FACTA: Finding Associated Concepts with Text Analysis – What diseases are related to a particular chemical? – What proteins are related to a particular disease? – etc. • EBIMed http://www.ebi.ac.uk/Rebholz-srv/ebimed/index.jsp • PubMatrix http://pubmatrix.grc.nia.nih.gov/ : • FACTA http://text0.mib.man.ac.uk/software/facta/ – Quick and interactive
    8. Query
    9. Click!
    10. Innovative Technologies applied to: • Term recognition • Named entity recognition Semantic Mark-up • Fact extraction ! semantic mark-up improves search ! classifying, linking documents ! knowledge discovery, hidden links, associations, hypothesis generation
    11. Natural Language Processing technologies • Part-of-speech tagging: GENIA – Tuned to biomedical text: 97-99% precision • Dictionary-based named-entity recognition • Deep parsing – Predicate argument relations (90%) • Protein-protein interaction extraction • Event / fact extraction
    12. Automatic Term Recognition http://www.nactem.ac.uk/software/termine/
    13. Recognising and Disambiguating Acronyms in Biomedical Literature http://www.nactem.ac.uk/software/acromine
    14. Named-entity recognition The peri-kappa B site mediates human immunodeficiency DNA virus virus type 2 enhancer activation in monocytes … cell_type ! Entity types (defined by Ontologies) \" Genes/protein names \" Enzymes, substances, metabolites, etc \" GO ontology, KEGG, CheBI, etc
    15. Leveraging resources • Annotated texts (GENIA corpus, GENIA event corpus) • Resources for bio-text mining – resource-building NLP tools for text-based knowledge harvesting (NaCTeM) – BioLexicon • Over 1.5M lexical entries for bio-text mining and growing…. • Containing rich linguistic information for bio-text mining
    16. Population Process Existing repositories chemical, disease, enzyme, species names Subclustering gene/protein names of term variants new gene/protein names Medline abstracts Named entity Term mapping recognition by normalization Bio-Lexicon terminological verbs Manual curation on-going Verb subcategorization verb subcategorization frames
    17. Semantic search based on facts • MEDIE: an interactive advanced IR system retrieving facts • Performs a semantic search ! Core technology annotates texts \" GENIA tagger \" syntactic structures \" Enju (deep parser) \" facts \" Dictionary-based named entity recognition J. Tsujii
    18. Medie system overview Off-line On-line Deep parser Semantically- RegionAlgebra Input Textbase annotated Search engine Entity Textbase Recognizer Search Query results
    19. Sentence Retrieval System Using Semantic Representation MEDIE
    20. InfoPubMed ! An interactive Information Extraction system and an efficient PubMed search tool, helping users to find information about biomedical entities such as genes, proteins, and the interactions between them. ! System components \" Deep parsing technology \" Extraction of protein-protein interactions \" Multi-window interface on a browser
    21. InfoPubMed Interactions and not just co-occurrences. Calculated using ML and deep semantics.
    22. Semantic Information Retrieval http://nactem4.mc.man.ac.uk:8080/Kleio/ # KLEIO: a semantically enriched information retrieval system for biology # Offers textual and metadata searches across MEDLINE # Leverages terminology technologies #Named entity recognition: gene, protein, metabolite, organ, disease, symptom
    23. KLEIO architecture
    24. Fewer documents with more precise query
    25. Linking and enriching pathways with text – REFINE (BBSRC) \" MCISB and NaCTeM (Kell, Ananiadou, Tsujii) – to integrate text mining techniques with visualisation technologies for better understanding of the evidence for biochemical and signalling pathways – to enrich pathway models encoded in the Systems Biology Markup Language (SBML) with evidence derived from text mining
    26. 2 Steps for linking text with pathways IkB P IkB U ! IkB Pathways Pathway Construction IkB IkB P Biological events IkB IkB U IkB ! Event Extraction … IkappaB is phosphorylated … Literature … Ikappa B ubiquitination … … degradation of IkB… Tsujii-lab, Tokyo
    27. Event Annotation - Example
    28. Statistics & References ! Statistics \" 36,114 events have been identified from and annotated to ! 1,000 Medline abstracts, which contain ! 9,372 sentences \" Kim, Jin-Dong, Tomoko Ohta and Jun'ichi Tsujii (2008) Corpus annotation for mining biomedical events from literature. BMC Bioinformatics \" http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA
    29. Acknowledgements • Junichi Tsujii and his lab (University of Tokyo) MEDIE, InfoPubMed, event annotation • Yoshimasa Tsuruoka (NER, FACTA, KLEIO, REFINE) • Naoaki Okazaki (TerMine, AcroMine) • Yutaka Sasaki (BioLexicon, NER, KLEIO) • John McNaught (BioLexicon, BOOTStrep project) • Chikashi Nobata (KLEIO) • Douglas Kell (REFINE)

    + Duncan HullDuncan Hull, 2 years ago

    custom

    2638 views, 10 favs, 0 embeds more stats

    presentation by Sophia Ananiadou at the Cheminform more

    More info about this document

    CC Attribution-ShareAlike LicenseCC Attribution-ShareAlike License

    Go to text version

    • Total Views 2638
      • 2638 on SlideShare
      • 0 from embeds
    • Comments 0
    • Favorites 10
    • Downloads 91
    Most viewed embeds

    more

    All embeds

    less

    Flagged as inappropriate Flag as inappropriate
    Flag as inappropriate

    Select your reason for flagging this presentation as inappropriate. If needed, use the feedback form to let us know more details.

    Cancel
    File a copyright complaint
    Having problems? Go to our helpdesk?

    Categories