This paper describes AIMS (Assisted Indexing atMississippi State), a system that aids human document analystsin the assignment of indexes to physical chemistry journalarticles. There are two major components of AIMS—a naturallanguage processing (NLP) component and an index generation (IG)component. The focus of this article is the IG. We describethe techniques and structures used by the IG in the selection ofappropriate indexes for a given article. We also describe theresults of evaluations of the system in terms of recall,precision, and overgeneration. We provide a description of agraphical user interface that we have developed for AIMS.Finally, we discuss future work.

This report describes the design of an object-oriented database system that was developedby scientists at the Mississippi State University Diagnostic and Instrumentation Laboratory incollaboration with scientists at the Idaho National Engineering Laboratory. The database has beendesigned to store data to be used by an expert system that analyzes and characterizes containerizedradiological waste. This expert system is to be a component of a larger system called Waste AssayMeasurement...

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.

Abstract Researchers at the Mississippi State University Diagnostic and Instrumentation Laboratory (DIAL) are collaborating with scientists at the Idaho National Engineering Laboratory (INEL) to develop an expert system that analyzes and characterizes containerized radiological waste. The DIAL researchers are making,use of various artificial intelligence (AI) techniques to develop different versions of the expert system, with the intent being the determination of the most appropriate AI technique or combination of techniques for this particular problem. This report provides an overview of fuzzy logic and describes its use in the implementation,of a preliminary version of the waste characterizationexpert system.

This report describes the results of a set of experiments conducted with AIMS (Assisted Indexing at Mississippi State), a system that aids human document analysts in the assignment of indexes to physical chemistry journal articles. We defined four different experiments using four different combinations of portions of an article (such as the title and the abstract) to determine which portions of an article were most useful in the generation of indexes. Using a testbed of about twenty articles, we generated indexes for each article, then compared the AIMS-generated indexes with the indexes that had previously been assigned to the articles by the document analysts. The indexes generated by the document analysts are considered to be the "correct" indexes. We measured the success of AIMS in generating the correct indexes by making use of measurements from the area of information retrieval - recall, precision, and overgeneration. In this report, we describe the various experiments and the success rates for AIMS in each of them.

In this report, we describe the development of an automated system that aids human document analysts in the assignment of indexes to journal articles in the area of physical chemistry. AIMS (Assisted Indexing at Mississippi State) has two major components: a natural language processing (NLP) component and an index generation (IG) component. This report provides an overview of each component. The description of the NLP component focuses on the identification of units of the text and the attachment of appropriate tags to terms in the text. Primarily, there are two kinds of units of interest: phrasal units (such as prepositional phrases) and word collocations in which the "multi-word" may differ semantically from any of the individual words that make up the multi-word (e.g., "atmospheric pressure"). The tags include both syntactic and semantic tags (e.g.: noun/chemical substance). The discussion of the IG component describes how this component makes use of the parsed articles produced by the NLP component and some domain-specific information such as a hierarchy of chemistry concepts to generate a set of recommended indexes for a given article.

AIMS (Assisted Indexing at Mississippi State) is a system that aids human document analysts in the assignment of indexes to physical chemistry journal articles. There are two major components of AIMS - a natural language processing (NLP) component and an index generation (IG) component. This report describes the approach used by the IG component in the generation of appropriate indexes for a given article. It provides in some detail a description of the algorithm used by the IG to generate candidate indexes and then determine which among the set of candidates are the most relevant concepts addressed in the article. It also provides a brief discussion of the graphical user interface that has been developed for the AIMS system.