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APPEMDIX A

THE SMART SYSTEMThe SMART automatic document retrieval system currently
running on the IBM 7094 digital computer at Harvard University wasused both as a simulation environment and data base generator for theexperimental results presented in this thesis. As the SMART system
has been thoroughly documented (references 1-3)> only a brief summaryof its main features is outlined here.

A.

Content Analysis Techniques

The indexing function of the SMART system is capable ofincorporating a number of automatic content analysis techniques. Documents are entered into the system in the natural language (with aminimal number of keypunching conventions) and passed through adictionary lookup phase. The lookup operate.s with a stem-suffix
splitting algorithm (which incorporates spelling rules), and word .
stems are matched against entries of a stored dictionary.

A varietyof dictionaries may be used in the system ranging from a simple one toone encoding (keyword dictionary) to a dictionary which produces amany to many thesaurus-type mapping.

In addition to providing asemantic encoding for the detected stems, the lookup process hasprovisions for providing syntactic stem codes based on both the stemand suffix dictionaries. After the initial lookup phase, a codedA-2

sentence by sentence text image is available for additional-content
analysis.

The principal processes available at this stage are phrase

identification procedures which may be based on an automatic syntactic
analysis or on a simple term-term co-occurrence detection scheme.
At the conclusion of the semantic coding process, the sentenceby sentence text image is compressed into a weighted property vector ..
index image. Property weights are derived by a summation over theencoded text image so that the weight of a given component of the vector
index image of a document is representative of the frequency ofoccurrence in the document of the features mapped into that component.
To reflect the multiple mappings incorporated in the thesaurus
transformation, each input term is mapped with a constant total weight.
Thus a-term which is encoded into a single thesaurus * category contributes
a weight w (w is a scale factor equal to 12 in the current system). If
the input term maps into k categories', each category receives..a contribution of w/k to its final weight. An occurrence of the term "band" ofFigure 2.1 (chapter 2),. for example, would contribute a weight of 12 tocategory 30> while an occurrence of the term "carrier" would contribute^eights of 6 to concepts 61 and y\ 6. This technique prevents ambiguousterms (terms which map into several categories) from distorting the. concept weights of the final- index vector. While the property vector
is the primary index language of the system, a number of alternative
component weighting schemes are possible, including the option of•ignoring all frequency derived information (which produces, in effect,a. set-represented text image).

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Document index images generated by this process may besubjected to a number of additional modifications. A variety oftransformations based on a pre-specified hierarchical structuring of
the elements of the index language is provided. Alternatively, .. .
relations among index terms derived from statistical associations ina given collection may be used for modifying index images. The
system, therefore, may provide a variety of representations for input
documents based on the initial dictionary lookup and subsequenttransformation rules defined on the index language.

(Note that theindex images used experimentally for this thesis were generated by alookup using version 2 of the SMART thesaurus with no. phrase
detection and no additional semantic transformations.)

B.

Search Request. Formulation

Search requests in the SMART system are introduced-directlyin the natural language and may be treated exactly as are document
texts. Requests, therefore, may be subjected to all or any subset ofthe content analysis procedures available for document processing.
In addition to varying the index image of a search request by thesequence of analysis procedures to which it is subjected, a number of
additional query modification procedures^ (including the relevance
feedback technique discussed in chapter 3) are being considered for .
inclusion into the system.

C. Query-Document Matching
The flexibility provided by. the computer allows the SMART

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sytem to incorporate a variety of query-do c-ument matching functions. In
addition to the cosine correlation measure for vector mode images, .
several correlation measures suitable for set mode index images areprovided.

Additionally, new index image comparison functions may beeasily programmed for experimental purposes.

D.

Evaluation

Assuming that relevance judgments are available for test
search requests, SMRT has the capability of automatically generating avariety of evaluation measures for each retrieval operation, including
those discussed in section 4 of chapter 5*

Additionally, the systemprovides sufficient output such that auxiliary programs can produce
system evaluations over a number of searches,, i.e. parameter averages,average precision vs. average recall plots, etc. Novel evaluation
algorithms may, therefore,' be applied to a number of searches byconstruction of computer programs to process such data.

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REFERENCES

Salton, G., "Information Storage.and Retrieval", Reports ISR 7,
ISR 8, and ISR 9/.The. National Science Foundation, Harvard
Computation Laboratory, June 1964> Dec. 19^4, and Sept. 1965
Salton, G., and Lesk, M., "The SMART.Automatic Document Retrieval
System- An Illustration", Communications of the ACM, Vol. 8, No. 6,
June 1965
Salton, G., "A Document Retrieval System for Man-MachineInteraction"j Proceedings of the ACM 19th National Conference,
Philadelphia, Pa., 1964
Rocchio, J., and Salton, G., "Information Search Optimization
and Iterative Retrieval Techniques", AFIPS Conference Proceedings,
Vol. 27, Part 1, Spartan Books, Washington, D.C., 1965