Boris A. Galitsky

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We develop a graph representation and learning technique for parse structures for paragraphs of text. We introduce Parse Thicket (PT) as a sum of syntactic parse trees augmented by a number of arcs for inter-sentence word-word relations such as co-reference and taxonomic relations. These arcs are also derived from other sources, including Speech Act and(More)
One of the main problems to be solved while assisting inter-human conflict resolution is how to reuse the previous experience with similar agents. A machine learning technique for handling scenarios of interaction between conflicting human agents is proposed. Scenarios are represented by directed graphs with labelled vertices (for communicative actions) and(More)
We develop the means to mine for associative features in biological data. The hybrid reasoning schema for deterministic machine learning and its implementation via logic programming is presented. The methodology of mining for correlation between features is illustrated by the prediction tasks for protein secondary structure and phylogenetic profiles. The(More)
We build an open-source toolkit which implements deterministic learning to support search and text classification tasks. We extend the mechanism of logical generalization towards syntactic parse trees and attempt to detect weak semantic signals from them. Generalization of syntactic parse tree as a syntactic similarity measure is defined as the set of(More)
A machine learning technique for handling scenarios of interaction between conflicting agents is suggested. Scenarios are represented by directed graphs with labeled vertices (for mental actions) and arcs (for temporal and causal relationships between these actions and their parameters). The relation between mental actions and their descriptions gives rise(More)
In this paper, we apply concept learning techniques to solve a number of problems in the customer relationship management (CRM) domain. We present a concept learning technique to tackle common scenarios of interaction between conflicting human agents (such as customers and customer support representatives). Scenarios are represented by directed graphs with(More)