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Text mining tools for semantically enriching scientific literature


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presentation by Sophia Ananiadou at the Cheminformatics workshop 4th March 2008

Published in: Education, Technology

Text mining tools for semantically enriching scientific literature

  1. 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. 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. 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. 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. 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. 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. 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 • PubMatrix : • FACTA – Quick and interactive
  8. 8. Query
  9. 9. Click!
  10. 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. 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. 12. Automatic Term Recognition
  13. 13. Recognising and Disambiguating Acronyms in Biomedical Literature
  14. 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) quot; Genes/protein names quot; Enzymes, substances, metabolites, etc quot; GO ontology, KEGG, CheBI, etc
  15. 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. 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. 17. Semantic search based on facts • MEDIE: an interactive advanced IR system retrieving facts • Performs a semantic search ! Core technology annotates texts quot; GENIA tagger quot; syntactic structures quot; Enju (deep parser) quot; facts quot; Dictionary-based named entity recognition J. Tsujii
  18. 18. Medie system overview Off-line On-line Deep parser Semantically- RegionAlgebra Input Textbase annotated Search engine Entity Textbase Recognizer Search Query results
  19. 19. Sentence Retrieval System Using Semantic Representation MEDIE
  20. 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 quot; Deep parsing technology quot; Extraction of protein-protein interactions quot; Multi-window interface on a browser
  21. 21. InfoPubMed Interactions and not just co-occurrences. Calculated using ML and deep semantics.
  22. 22. Semantic Information Retrieval # 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. 23. KLEIO architecture
  24. 24. Fewer documents with more precise query
  25. 25. Linking and enriching pathways with text – REFINE (BBSRC) quot; 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. 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. 27. Event Annotation - Example
  28. 28. Statistics & References ! Statistics quot; 36,114 events have been identified from and annotated to ! 1,000 Medline abstracts, which contain ! 9,372 sentences quot; Kim, Jin-Dong, Tomoko Ohta and Jun'ichi Tsujii (2008) Corpus annotation for mining biomedical events from literature. BMC Bioinformatics quot;
  29. 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)