Semi automatic indexing of PostScript files using Medical Text Indexer in medical education.
At Albert Einstein College of Medicine a large part of online lecture materials contain PostScript files. As the collection grows it becomes essential to create a digital library to have easy access to relevant sections of the lecture material that is full-text indexed; to create this index it is necessary to extract all the text from the document files that constitute the originals of the lectures. In this study we present a semi automatic indexing method using robust technique for extracting text from PostScript files and National Library of Medicine's Medical Text Indexer (MTI) program for indexing the text. This model can be applied to other medical schools for indexing purposes.
Mollah
Shamim Ara
SA
Albert Einstein College of Medicine.
Cimino
Christopher
C
D000043:Abstracting and Indexing / Q000379:methods*; D046609:Libraries, Digital; D046650:Medical Subject Headings; D009323:Natural Language Processing