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Automated Indexing Research

Project information
Researchers: 

The Indexing Initiative (II) project investigates language-based and machine learning methods for the automatic selection of subject headings for use in both semi-automated and fully automated indexing environments at NLM. Its major goal is to facilitate the retrieval of biomedical information from textual databases such as MEDLINE.

Team members have developed an indexing system, Medical Text Indexer (MTI), based on two fundamental indexing methodologies. The first of these calls on the MetaMap program to map citation text to concepts in the UMLS Metathesaurus which are then restricted to MeSH headings. The second approachuses the MeSH headings from the PubMed related articles which are precomputed by PubMed. Results from the two basic methods are combined into a ranked list of recommended indexing terms, incorporating aspects of MEDLINE indexing policy in the process. The  MTI is used by NLM Indexers, Cataloging, and the NLM History of Medicine book collection. Recently MTI also became the first-line indexer for a set of 23 journals.

The II team worked closely with an NLM Associate Fellow whose ongoing project was designed to investigate the feasibility of automating the creation of functional annotations about genes, known as Gene Reference into Function (geneRIF). We have developed a prototype, the Gene Indexing Assistant (GIA), and integrated it into the Data Creation Management System used by the Indexers for testing and evaluation.

MetaMap is a critical component of the MTI system and used worldwide in bioinformatics research. MetaMap is one of the NLM resources integrated into IBM’s Watson system for healthcare applications. Recent work has improved processing speed significantly, added XML (eXtensible Markup Language) output, implemented negation identification, and enabled users to supply their own acronyms/abbreviations list. MetaMap is available on Windows, Macintosh and Linux platforms. Users can build their own data sets with the MetaMap Data File Builder and access their local version of MetaMap via either an embedded Java API (Application Programming Interface) or UIMA (Unstructured Information Management Architecture) wrapper.

Taking a Word Sense Disambiguation (WSD) approach, the context words surrounding the ambiguous word are compared to a profile built from each of the UMLS concepts linked to the ambiguous term being disambiguated. This approach has been previously used in the biomedical domain with the NLM WSD corpus. A concept profile vector has as dimensions the tokens obtained from the concept definition or definitions if available, synonyms, and related concepts excluding siblings. Stop words are discarded, and Porter stemming is used to normalize the tokens. In addition, the token frequency is normalized based on the inverted concept frequency so that terms which are repeated many times within the UMLS will have less relevance. A context vector for an ambiguous term includes the term frequency; stop words are removed and the Porter stemmer is applied. The word order is lost in the conversion. Profile Vectors of candidate concepts linked to an ambiguous word are compared to the context of the ambiguous word using cosine similarity; the concept with the highest cosine similarity is selected.

Publications/Tools: 
Aronson AR, Mork JG, Lang FM, Rogers WJ, Neveol A. NLM Medical Text Indexer: A Tool for Automatic and Assisted Indexing April 2008 Technical Report to the LHNCBC Board of Scientific Counselors.
Demner-Fushman D, Humphrey SM, Ide NC, Loane RF, Mork J, Ruch P, Ruiz ME, Smith LH, Wilbur WJ, Aronson AR. Combining Resources to Find Answers to Biomedical Questions Proc TREC 2007, 205-14
Névéol A, Mork JG, Aronson AR. Automatic indexing of specialized documents: using generic vs. domain-specific document representations. Proc 2007 Workshop BioNL'07, 183-92.
Névéol A, Shooshan SE, Mork JG, Aronson AR. Fine-grained indexing of the biomedical literature: MeSH subheading attachment for a MEDLINE indexing tool. AMIA Annu Symp Proc. 2007 Oct 11:553-7.
Ruch P, Geissbuhler A, Gobeill J, Lisacek F, Tbahriti I, Veuthey A, Aronson AR. Using Discourse Analysis to Improve Text Categorization in MEDLINE Stud Health Technol Inform. 2007;129(Pt 1):710-5
Aronson AR, Bodenreider O, Demner-Fushman D, Fung KW, Lee VK, Mork JG, Névéol A, Peters L, Rogers WJ. From indexing the biomedical literature to coding clinical text: experience with MTI and machine learning approaches. Proc 2007 Workshop BioNL'07, June 2007, pp. 105-112, Prague, Czech Republic.
Keselman A, Tse T, Crowell J, Browne AC, Ngo L, Zeng Q. Assessing Consumer Health Vocabulary Familiarity: An Exploratory Study J Med Internet Res. 2007 Mar 14;9(1):e5
Névéol A, Shooshan SE, Humphrey SM, Rindflesch TC, Aronson AR. Multiple approaches to fine-grained indexing of the biomedical literature. Pac Symp Biocomput. 2007:292-303.
He Y, Kayaalp M. Interpretation of Data and Identification of Information are Sine Qua Non for Modern Digital Information Services AMIA Annu Symp Proc. 2006:946
Ruch P, Tbahriti I, Bobeill J, Aronson AR. Argumentative Feedback: A Linguistically-motivated Term Expansion for Information Retrieval Proc COLING/ACL 2006, 675-82

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