US 11,989,261 B2
Answering questions with artificial intelligence using tabular data
Mustafa Canim, San Jose, CA (US); Michael Robert Glass, Bayonne, NJ (US); Alfio Massimiliano Gliozzo, Brooklyn, NY (US); and Nicolas Rodolfo Fauceglia, Brooklyn, NY (US)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Sep. 30, 2020, as Appl. No. 17/039,379.
Prior Publication US 2022/0101052 A1, Mar. 31, 2022
Int. Cl. G06K 9/62 (2022.01); G06F 16/22 (2019.01); G06F 16/2455 (2019.01); G06F 16/248 (2019.01); G06F 17/18 (2006.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06N 5/04 (2023.01)
CPC G06F 18/2148 (2023.01) [G06F 16/221 (2019.01); G06F 16/2282 (2019.01); G06F 16/2455 (2019.01); G06F 16/248 (2019.01); G06F 17/18 (2013.01); G06F 18/2185 (2023.01); G06N 5/04 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method to answer a question using a data table, comprising:
receiving, by said computer, a user question and a target table containing a target cell corresponding to a target answer for said user question, said target cell corresponding to a target column and a target row;
generating, by said computer, a first classifier adapted to provide column correlation values reflecting the probability that a given column is said target column;
generating, by said computer, a second classifier adapted to provide row correlation values reflecting the probability that a given row is said target row;
applying, by said computer, said first classifier to the columns in the target table to determine a column correlation value for each column;
applying, by said computer, said second classifier to the rows in the target table to determine a row correlation value for each row;
suggesting, by said computer, as the target cell, a cell having elevated column and row correlation values relative to other cells in the target table;
receiving, by said computer, a set of training data; and
fine-tuning, by said computer using said training data, said first classifier and said second classifier.