International Business Machines Corporation

Avinash Balakrishnan

Ruchir Puri

Achille Belly Fokoue-Nkoutche

Pin-Yu Chen

Amit Dhurandhar

Karthikeyan Shanmugam

Maria Chang

Chulaka Gunasekara

Kartik Talamadupula

Kshitij P Fadnis

Pavan Kapanipathi Bangalore

Ibrahim Abdelaziz

Bassem Makni

Tejaswini Pedapati

Veronika Thost

Nicholas Mattei

Siva Sankalp Patel

A method, system, and computer program product, including generating a contrastive explanation for a decision of a classifier trained on structured data, highlighting an important feature that justifies the decision, and determining a minimal set of new values for features that alter the decision.

Model agnostic contrastive explanations for structured data

One embodiment of the invention provides a method for natural language processing (NLP). The method comprises extracting knowledge outside of text content of a NLP instance by extracting a set of subgraphs from a knowledge graph associated with the text content. The set of subgraphs comprises the knowledge. The method further comprises encoding the knowledge with the text content into a fixed size graph representation by filtering and encoding the set of subgraphs. The method further comprises applying a text embedding algorithm to the text content to generate a fixed size text representation, and classifying the text content based on the fixed size graph representation and the fixed size text representation.

Infusing knowledge into natural language processing tasks using graph structures

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Elmsford, NY, United States of America

Avinash Balakrishnan