Prof. Boris Galitsky
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Academic Career Length
35Years (1992-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 February 2009 in Decision Support Systems
Journal Article
Boris GalitskyMGMaría P. González
María P. González
CCCarlos Iván Iván Chesñevar
Carlos Iván Iván ChesñevarAutomating customer complaints processing is a major issue in the context of knowledge management technologies for most companies nowadays. Automated decision-support systems are important for complaint processing, integrating human experience in understanding complaints and the application of machine learning techniques. In this context, a major challenge in complaint processing involves assessing the validity of a customer complaint on the basis of the emerging dialogue between a customer and a company representative. This paper presents a novel approach for modelling and classifying complaint scenarios associated with customer-company dialogues. Such dialogues are formalized as labelled graphs, in which both company and customer interact through communicative actions, providing arguments that support their points. We show that such argumentation provides a complement to perform machine learning reasoning on communicative actions, improving the resulting classification accuracy
1 November 2012 in Data & Knowledge Engineering
Journal Article
Boris Galitsky
Josep Lluis de la Rosa EstevaGDGábor Dobrocsi
Gábor Dobrocsi
9 October 2008 in Journal of Experimental & Theoretical Artificial Intelligence
Journal Article
Boris GalitskySKSergei O. Kuznetsov
Sergei O. Kuznetsov
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 arcs (for temporal and causal relationships between these actions and their parameters). For illustrative purposes, classification of a scenario is computed by comparing partial matching of its graph with graphs of positive and negative examples. Nearest Neighbour learning is followed by the JSM-based learning which minimised the number of false negatives and takes advantage of a more accurate way of matching sequences of communicative actions. Developed scenario representation and comparative analysis techniques are applied to the classification of textual customer complaints. It is shown that analysing the structure of communicative actions without context information is frequently sufficient to estimate complaint validity. Therefore, being domain-independent, proposed machine learning technique is a good compliment to a wide range of customer relation management applications where formal treatment of inter-human interactions is required in a decision-support mode.
About Me
Boris Galitsky contributed linguistic and machine learning technologies to Silicon Valley startups as well as companies like eBay and Oracle for over 25 years. Boris’ information extraction and sentiment analysis techniques assisted a number of acquisitions, such as Xoopit by Yahoo, Uptake by Groupon, Loglogic by Tibco and Zvents by eBay. His security-related technologies of document analysis contributed to acquisition of Elastica by Semantec. https://github.com/bgalitsky/relevance-based-on-parse-trees
As an architect of the Intelligent Bots project at Oracle, Boris developed a discourse analysis technique user for dialogue management and published in the book "Developing Enterprise Chatbots”. He also published a two-volume monograph “AI for CRM”, based on his experience developing Oracle Digital Assistant. Boris is Apache committer to OpenNLP where he created OpenNLP. Similarity component which is a basis for a semantically-enriched search engine and chatbot development.
Galitsky’s exploration and formalization of human seasoning culminated in the book “Computational Autism” broadly used by parents of children with autistic reasoning and rehabilitation personnel. Boris's focus on medical domain led to another research monograph, “AI for Health Applications and Management”.
https://www.amazon.com/Books-Boris-Galitsky/s?rh=n%3A283155%2Cp_27%3ABoris+Galitsky