International Business Machines Corporation

Monvorath Phongpaibul

Yan Luo

Gary Mazo

Seana Hogan

John Carl DelMonaco

Hongxia Li

Tala El Hallak

Rahamim Katan

Stanislav Georgiev

Techniques for context-based keyword grouping for business rule mining are described herein. An aspect includes determining, based on a first corpus, a first list of keywords. Another aspect includes constructing a co-occurrence matrix based on the first list of keywords. Another aspect includes applying a clustering algorithm to the co-occurrence matrix to determine a first plurality of keyword groups. Another aspect includes presenting the first plurality of keyword groups to a user via a user interface.

Context-based keyword grouping

A computer-implemented method includes training a classification model to produce a mapping from a set of feature vectors to a set of confidence levels. Each feature vector describes a respective term, and each confidence level indicates a likelihood that the respective term is a keyword. A plurality of program artifacts are tokenized into a plurality of terms. For each term in the plurality of terms, a respective feature vector is determined for describing the term, based on the plurality of program artifacts. For each term in the plurality of terms, based on the respective feature vector, using the mapping, a respective confidence level is determined for indicating a likelihood that the term is a keyword of the plurality of program artifacts.

Predicting keywords in an application

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Monvorath Phongpaibul