US 12,436,981 B2
Transmforming table-to-text using agglomerative clustering
Kunal Sawarkar, Franklin Park, NJ (US)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed on Dec. 13, 2022, as Appl. No. 18/065,621.
Prior Publication US 2024/0193191 A1, Jun. 13, 2024
Int. Cl. G06F 16/334 (2025.01); G06F 40/10 (2020.01); G06N 20/00 (2019.01)
CPC G06F 16/3344 (2019.01) [G06F 40/10 (2020.01); G06N 20/00 (2019.01)] 17 Claims
OG exemplary drawing
 
1. A method, comprising:
ingesting, by a computing system, a data set that includes at least one structured hierarchical or multidimensional table for a particular domain;
processing, by the computing system, the ingested data set that includes the at least one structured hierarchical or multidimensional table for the particular domain by applying a generated machine learning model;
generating, by the computing system, inferential natural language text based on applying the machine learning model;
assigning, by the computing system, a row identifier and a unique token for each row of the at least one structured hierarchical or multidimensional table;
converting, by the computing system, each row of the at least one structured hierarchical or multidimensional table into a sentence with the assigned row identifier and unique token;
mapping, by the computing system, using agglomerative clustering techniques, the at least one structured hierarchical or multidimensional table and an associated hierarchical structure;
determining, by the computing system, based on mapping the at least one structured hierarchical or multidimensional table and the associated hierarchical structure, context for each row of the at least one structured hierarchical or multidimensional table;
determining, based on the determined context for each row of the at least one table, a relationship between each row of the at least one structured hierarchical or multidimensional table; and
outputting, by the computing system, the generated inferential natural language text in a sequence format.