The SMART (System for the Mechanical Analysis and Retrieval of Text) Information Retrieval System is an information retrieval system developed at Cornell University in the 1960s.[1] Many important concepts in information retrieval were developed as part of research on the SMART system, including the vector space model, relevance feedback, and Rocchio classification.
Gerard Salton led the group that developed SMART. Other contributors included Mike Lesk.
The SMART system also provides a set of corpora, queries and reference rankings, taken from different subjects, notably
ADI: publications from information science reviews
Computer science
Cranfield collection: publications from aeronautic reviews
Forensic science: library science
MEDLARS collection: publications from medical reviews
Time magazine collection: archives of the generalist review Time in 1963
To the legacy of the SMART system belongs the so-called SMART triple notation, a mnemonic scheme for denoting tf-idf weighting variants in the vector space model. The mnemonic for representing a combination of weights takes the form ddd.qqq, where the first three letters represents the term weighting of the collection document vector and the second three letters represents the term weighting for the query document vector. For example, ltc.lnn represents the ltc weighting applied to a collection document and the lnn weighting applied to a query document.
The following tables establish the SMART notation:[2]
Symbols and notation
represents a document vector, where is the weight of the term in and is the number of unique terms in . Positive features characterize terms that are present in a document, and the weight of zero is used for terms that are absent from a document.
Occurrence frequency of term in document
Number of unique terms in document
Number of collection documents
Average number of unique terms in a document
Number of documents with term present
Number of characters in document
Occurrence frequency of the most common term in document
Average number of characters in a document
Average occurrence frequency of a term in document
Global collection statistics
The slope in the context of pivoted document length normalization[3]
Smart term-weighting triple notation
Term frequency
Document frequency
Document length normalization
b
Binary weight
x
n
Disregards the collection frequency
x
n
No document length normalization
t
n
Raw term frequency
f
Inverse collection frequency
c
Cosine normalization
a
Augmented normalized term frequency
t
Inverse collection frequency
u
Pivoted unique normalization[3]
l
Logarithm
p
Probabilistic inverse collection frequency
b
Pivoted characted length normalization[3]
L
Average-term-frequency-based normalization[3]
d
Double logarithm
The gray letters in the first, fifth, and ninth columns are the scheme used by Salton and Buckley in their 1988 paper.[4] The bold letters in the second, sixth, and tenth columns are the scheme used in experiments reported thereafter.
^Salton, G, Lesk, M.E. (June 1965). "The SMART automatic document retrieval systems—an illustration". Communications of the ACM. 8 (6): 391–398. doi:10.1145/364955.364990.{{cite journal}}: CS1 maint: multiple names: authors list (link)
^Palchowdhury, Sauparna (2016). "On The Provenance of tf-idf". sauparna.sdf.org. Retrieved 2019-07-29.
^ abcdSinghal, A., Buckley, C., & Mitra, M. (1996). Pivoted Document Length Normalization. SIGIR Forum, 51, 176-184.
^Salton, G., & Buckley, C. (1988). Term-Weighting Approaches in Automatic Text Retrieval. Inf. Process. Manage., 24, 513-523.
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