An automated system that assists in the generation of document indexes

Authors:
Julia Hodges;Shiyun Yie;Ray Reighart;Lois Boggess
Affiliations:
Box 9637, Mississippi State, MS 39762–9637, USA;Box 9637, Mississippi State, MS 39762–9637, USA;Department 7, Chemical Abstracts Service, 2540 Olentangy River Road, Columbus, OH 43202-1505, USA;Box 9637, Mississippi State, MS 39762–9637, USA
Venue:
Natural Language Engineering
Year:
1996

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Abstract

In this article, we describe AIMS (Assisted Indexing at Mississippi State), a system intended to aid human document analysts in the assignment of indexes to physical chemistry journal articles. The two major components of AIMS are a natural language processing (NLP) component and an index generation (IG) component. We provide an overview of what each of these components does and how it works. We also present the results of a recent evaluation of our system in terms of recall and precision. The recall rate is the proportion of the ‘correct’ indexes (i.e. those produced by human document analysts) generated by AIMS. The precision rate is the proportion of the generated indexes that is correct. Finally, we describe some of the future work planned for this project.