hive as a machine aided indexing tool n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
HIVE as a Machine-aided Indexing Tool PowerPoint Presentation
Download Presentation
HIVE as a Machine-aided Indexing Tool

Loading in 2 Seconds...

play fullscreen
1 / 4

HIVE as a Machine-aided Indexing Tool - PowerPoint PPT Presentation


  • 77 Views
  • Uploaded on

HIVE as a Machine-aided Indexing Tool. Personal Keyword use without vocabulary control Machine-aided indexing term extraction Participant relevant and not relevant judgments Inter-indexing consistency Rolling’s Measure Hooper’s Measure. Organizing Scientific Data Sets.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'HIVE as a Machine-aided Indexing Tool' - magee-valdez


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
hive as a machine aided indexing tool
HIVE as a Machine-aided Indexing Tool

Personal Keyword use without vocabulary control

Machine-aided indexing term extraction

Participant relevant and not relevant judgments

Inter-indexing consistency

Rolling’s Measure

Hooper’s Measure

hive dryad evaluation
HIVE/Dryad Evaluation

Craig Willis, Hollie White, Lee Richardson, Casey Rawson

Jane Greenberg, Bob Losee, Ryan Scherle, Todd Vision

  • Questions
    • Given Dryad article metadata (title, abstract, depositor-supplied keywords), what are the best approaches for term suggestion from selected controlled vocabularies (MeSH, ITIS, TGN)?
    • Can one approach be used for subject, taxonomic and geographic indexing?
  • Method
    • Create “gold standard” of manually index records based on mapping of Dryad, MEDLINE and BIOSIS Previews to MeSH, TGN, ITIS
    • Evaluate state-of-the-art techniques for automatic subject, and taxonomic, and geographic indexing
  • Preliminary results
    • For taxonomic name indexing, untrained KEA++ performs almost as well as state-of-the-art taxonomic name extraction (FindIt)
    • For geographic name indexing with TGN, simple graph-based ranking algorithm outperforms KEA++.
thesaurus walking automatic indexing with controlled vocabularies
Thesaurus Walking: Automatic Indexing with Controlled Vocabularies

Craig Willis, Bob Losee, Jane Greenberg

  • Questions
    • Starting from the location of terms in a document and moving to the indexer assigned controlled terms, how do indexers navigate in a thesaurus?
    • How can this knowledge be used to improve techniques for automatic indexing with controlled vocabularies?
    • How can this knowledge be used to improve thesauri?
  • Methodology
    • Unsupervised, graph-based approach using random walks on thesauri
  • Preliminary results
    • Indexer assigned controlled terms are identified at a rate much higher than random, but far from perfect.
    • Suggests that this method could best be used in combination with other dissimilar automatic indexing methods.