Lois Boggess

Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm

This paper presents the inception and subsequent revisions of an immune-inspired supervised learning algorithm, Artificial Immune Recognition System (AIRS). It presents the immunological components that inspired the algorithm and describes the initial algorithm in detail. The discussion then moves to revisions of the basic algorithm that remove certain unnecessary complications of the original version. Experimental results for both versions of the algorithm are discussed and these results indicate that the revisions to the algorithm do not sacrifice accuracy while increasing the data reduction capabilities of AIRS.

A resource limited artificial immune classifier

This paper presents a new supervised learning paradigm inspired by mechanisms exhibited in immune systems. This work provides an explication of a resource limited artificial immune classification algorithm, named AIRS (Artificial Immune Recognition System), and provides results on simulated data sets to demonstrate the fundamental behavior of the algorithm.

A SIMPLE BUT USEFUL APPROACH TO CONJUNCT IDENTIFICATION

This paper presents an approach to identifying conjuncts of coordinate conjunctions appearing in text which has been labelled with syntactic and semantic tags. The overall project of which this research is a part is also briefly discussed. The program was tested on a 10,000 word chapter of the Merck Veterinary Manual. The algorithm is deterministic and domain independent and it performs relatively well on a large real-life domain. Constructs not handled by the simple algorithm are also described in some detail.

An investigation into the source of power for AIRS, an artificial immune classification system

The AIRS classifier, based on metaphors from the field of artificial immune systems, has shown itself to be an effective general purpose classifier across a broad spectrum of classification problems. This research examines the new classifier empirically, replacing one of the two likely sources of its classification power with alternative modifications. The results are slightly less effective, but not statistically significantly so. We conclude that the modifications, which are computationally somewhat more efficient, provide fast test versions of AIRS for users to experiment with. We also conclude that the chief source of classification power of AIRS must lie in its replacement and maintenance of its memory cell population.

Non-Euclidean distance measures in AIRS, an artificial immune classification system

The AIRS classifier, based on principles derived from resource limited artificial immune systems, performs consistently well over a broad range of classification problems. This paper explores the effects of adding nonEuclidean distance measures to the basic AIRS algorithm using four well-known publicly available classification problems having various proportions of real, discrete, and nominal features.

An automated system that assists in the generation of document indexes

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.

Automated knowledge derivation: Domain-independent techniques for domain-restricted text sources

This article provides a description of the major components of a system that builds and updates a knowledge base by extracting the knowledge from natural language text. the knowledge extraction is done in a domain‐independent manner and does not rely on particular vocabulary or grammar constructions. the only restriction is that the input text must be technical text from some specific problem domain. an important capability of the system is that it can bootstrap itself. That is, beginning with only a description of the types of object and relationships to be stored in the knowledge base, the system can start with an empty knowledge base and build the knowledge base as it processes the text. the knowledge extraction systems success in extracting knowledge from various input texts was evaluated using scoring metrics reported by Lehnert and Sundheim [AI Mag., 12(3), 81–94 (1991)]. the initial results indicate that the knowledge extraction mechanism is both effective and independent of a particular authors writing style or a particular domain.

TWO SIMPLE PREDICTION ALGORITHMS TO FACILITATE TEXT PRODUCTION

Several simple prediction schemes are presented for systems intended to facilitate text production for handicapped individuals. The schemes are based on single-subject language models, where the system is self-adapting to the past language use of the subject. Sentence position, the immediately preceding one or two words, and initial letters of the desired word are cues which may be used by the systems.

A knowledge-based approach to indexing scientific text

We are developing a system which scans articles from scientific literature for the purpose of indexing the text. That is, the system should assist in the rapid determination of the key topics and content of articles in a particular domain and in the production of brief phrases describing the content. The number of correct concepts generated should be 80% of the concepts present (a recall rate of 80%), as compared to the output of human document analysts processing the same material.

Spatial operators in natural language understanding: the prepositions

The paper describes the operation of a LISP program which accepts English sentences involving spatial prepositions and creates a three dimensional model of the objects described, with emphasis on the appropriate spatial relations between the objects. A sequence of such sentences can result in a fairly elaborate model. The program can then answer questions about the relationship of the objects, even though the relationship in question between two objects in the model may not have been explicit in the original description.