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An automated system that assists in the generation of document indexes

Published online by Cambridge University Press:  01 June 1996

JULIA HODGES ,
SHIYUN YIE ,
RAY REIGHART  and
LOIS BOGGESS
JULIA HODGES
Affiliation:
Box 9637, Mississippi State, MS 39762–9637, USA
SHIYUN YIE
Affiliation:
Box 9637, Mississippi State, MS 39762–9637, USA
RAY REIGHART
Affiliation:
Department 7, Chemical Abstracts Service, 2540 Olentangy River Road, Columbus, OH 43202-1505, USA
LOIS BOGGESS
Affiliation:
Box 9637, Mississippi State, MS 39762–9637, USA
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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.

Type
Research Article
Information
Natural Language Engineering , Volume 2 , Issue 2 , June 1996 , pp. 137 - 160
Copyright
© 1996 Cambridge University Press

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