Abstract: A method is provided for analyzing and interpreting a dataset composed of electronic documents including free-form text. The method includes unifying terms of interest in the collection of terms of interest to identify variants of the terms of interest. This includes identifying candidate variants of a term of interest based on semantic similarity between the term of interest and other terms in the database, determined using an unsupervised machine learning algorithm. Linguistic features and contextual features of the term of interest and its candidate variants are extracted, at least the contextual features being extracted using the unsupervised machine learning algorithm. And a supervised machine learning algorithm is used with the linguistic features and contextual features to identify variants of the term of interest from the candidate variants, such as for application to generate features of the documents for data analytics performed thereon.

Abstract: A method is provided for analyzing and interpreting a dataset composed of electronic documents including free-form text. The method includes text mining the documents for terms of interest, including receiving a set of seed nouns as input to an iterative process an iteration of which includes searching for multiword terms having seed nouns as their head words, at least some of which define a training set of a machine learning algorithm used to identify additional multiword terms at least some of which have nouns outside the set of seed nouns as their head words. The iteration also includes adding the nouns outside the set of seed nouns to the set and thereby identifying a new set of seed nouns for a next iteration. The method includes unifying terms of interest to produce normalized terms of interest for application to generate features of the documents for data analytics performed thereon.

Abstract: A method is provided for analyzing and interpreting a dataset composed of electronic documents including free-form text. The method includes unifying terms of interest in the collection of terms of interest to identify variants of the terms of interest. This includes identifying candidate variants of a term of interest based on semantic similarity between the term of interest and other terms in the database, determined using an unsupervised machine learning algorithm. Linguistic features and contextual features of the term of interest and its candidate variants are extracted, at least the contextual features being extracted using the unsupervised machine learning algorithm. And a supervised machine learning algorithm is used with the linguistic features and contextual features to identify variants of the term of interest from the candidate variants, such as for application to generate features of the documents for data analytics performed thereon.

Abstract: A method is provided for analyzing and interpreting a dataset composed of electronic documents including free-form text. The method includes text mining the documents for terms of interest, including receiving a set of seed nouns as input to an iterative process an iteration of which includes searching for multiword terms having seed nouns as their head words, at least some of which define a training set of a machine learning algorithm used to identify additional multiword terms at least some of which have nouns outside the set of seed nouns as their head words. The iteration also includes adding the nouns outside the set of seed nouns to the set and thereby identifying a new set of seed nouns for a next iteration. The method includes unifying terms of interest to produce normalized terms of interest for application to generate features of the documents for data analytics performed thereon.