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There are many cases where we have a handful (10 - 2000) past examples of text data and we want to see if new text is close to these saved examples. Machine learning techniques like classification are not appropriate because we don’t have enough data to train an accurate model.
This operator builds a model for each group in the dataset so during matching we find best matches from the same group.
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Example
Input
table = github_logs
server | corpus | label | domain |
|---|---|---|---|
machine1 | a b c, d e f, g, h, i, j | x | |
machine1 | aa b, c, d ee, ff, gg, hh, i, jj | y | |
machine2 | k, l, m, n, o, p, q | z | apple |
LQL command
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buildModelFromCorpus(inputTable, "corpusModel", "server", "corpus", ["label", "domain"]) // table = inputTable // text to train model = corpus // columns to keep so they will be added after match is found = label and domain // minDF and minTF are default |
Output
RESULT |
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'Successfully created model and stored into <> file' |