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Mining External Resources for Biomedical IE

Mining External Resources for Biomedical IE. Why, How, What. Malvina Nissim mnissim@inf.ed.ac.uk. goal: Named Entity Recognition. Why. method: supervised learning. feature extraction. (text) internal features: word shape, n-grams,. protein-indicative features: - of shape a0a0a0a…

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Mining External Resources for Biomedical IE

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  1. Mining External Resourcesfor Biomedical IE Why, How, What Malvina Nissim mnissim@inf.ed.ac.uk

  2. goal: Named Entity Recognition Why • method: supervised learning • feature extraction • (text) internal features: word shape, n-grams, ... protein-indicative features: - of shape a0a0a0a… - followed by /bind/ - shorter than 5 characters • generalisations on training data might be incomplete • acquired evidence might be absent in test instance

  3. Getting Additional Evidence internal features might be insufficient, but good evidence might be somewhere else... • small and accurate lists of proteins (gazetteers) • use as rules • use as features • other texts might contain indicative n-grams • other texts might contain indicative n-grams • how to use other texts • which texts to use Note: some systems (MaxEnt for instance) can easily and successfully integrate a huge number of features

  4. How patterns “X gene/protein/DNA” “X sequence/motif” A. Create patterns (aim, method, input) B. Search corpus for patterns and obtain counts C. Use counts as appropriate

  5. 1. AIM 2. METHOD 3. INPUT Create Patterns (I) 1. AIM (granularity) distinguish entities from non-entities “X gene OR DNA OR protein” + bypass ambiguities and data sparseness – less information distinguish between entities “X gene” “X DNA” “X binds” + more information – ambiguities, data sparseness

  6. 1. AIM 2. METHOD 3. INPUT Create Patterns (II) 2. METHOD by hand (experts) + high precision, exact target – time consuming, experts needed automatically (collocations, clustering) + no human intervention – lower precision, not necessarily interesting patterns

  7. 1. AIM 2. METHOD 3. INPUT Create Patterns (III) 3. INPUT (“X gene”) low frequency words (as estimated from a non-specific corpus) words not found in standard dictionary NP chunks first output of classifier increase precision but lower recall prec rec f-score all features .813 .861 .836 – web .807 .864 .835

  8. What? Google vs PubMed • PubMed: searchable collection of over 12M biomedical abstracts, more sophisticated search options • Everything: Google searches over 8 billion pages, raw search, API “p53 gene” PubMed Google 5,843 documents ~165,000 pages

  9. Google + PubMed “anything you want” site:<specific_site> “p53 gene” site:www.ncbi.nlm.nih.gov Rob Futrelle has this function available on this webpage: http://www.ccs.neu.edu/home/futrelle/bionlp/search.html • comment: sometimes PubMed reports • “Quoted phrase not found” even when • Google finds the phrase. PubMed provides phrase search only on pre-indexed phrases

  10. PubMed > Google • query expansion PubMed uses the MeSH headings to match synonyms (it will expand “Pol II” to search for “DNA Polymerase II”) Google will only try correct misspelling • field specific search PubMed allows field-specific searches (eg year) Google cannot refine its search in this respect • timeliness PubMed is updated daily Google is slow in updating

  11. PubMed > Google (cont’d) • ranking Google does a ‘vote’-based ranking: not necessarily good PubMed does not do any ranking (possibly bad too...) • truncation and flexibility PubMed accepts truncated entries and will look for all possible Variations. It will try break phrases if no matches are found. Google has a rigid search • manual indexing PubMed’s MeSH contain keywords not necessarily contained in the abstract Google cannot find something that is not mentioned in the abstract

  12. What to Use?(or How to Use the Evidence) What to Use?(or How to Use the Evidence) • as a rule + sure identification of entities – too powerful -> high risk of false positives might be better to use PubMed: less info but precise • as a feature + less false positives + some systems (MaxEnt) can integrate huge number of features – might still not get used or provide enough evidence might be OK to use Google: more info but not necessarily precise

  13. iHOP (Information Hyperlinked Over Proteins)A gene network for navigating the literature Nature Genetics, Vol. 36(7), July 2004 http://www.pdg.cnb.uam.es/UniPub/iHOP • uses genes and proteins as hyperlinks between sentences • and abstracts http://www.pdg.cnb.uam.es/UniPub/iHOP • each step through the network produces information about • one single gene and its interactions • information retrieved by connecting similar concepts • precision of gene name and synonym identification: 87-99% • readers can still check correctness of sentences when they are • presented to them • shortest path between any 2 genes is on average 4 steps only

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