US 11,886,480 B2
Detecting affective characteristics of text with gated convolutional encoder-decoder framework
Kushal Chawla, Kadubeesanahalli (IN); Niyati Himanshu Chhaya, Hyderabad (IN); and Sopan Khosla, Kadubeesanahalli (IN)
Assigned to ADOBE INC., San Jose, CA (US)
Filed by Adobe Inc., San Jose, CA (US)
Filed on Aug. 29, 2022, as Appl. No. 17/822,837.
Application 17/822,837 is a continuation of application No. 16/224,501, filed on Dec. 18, 2018, granted, now 11,449,537.
Prior Publication US 2022/0414135 A1, Dec. 29, 2022
Int. Cl. G06F 16/35 (2019.01); G06N 3/08 (2023.01); G06N 3/04 (2023.01); G06F 40/279 (2020.01); G06F 40/10 (2020.01); G06N 20/00 (2019.01)
CPC G06F 16/35 (2019.01) [G06F 40/10 (2020.01); G06F 40/279 (2020.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method for identifying an affect label of text with a gated convolutional encoder-decoder model, wherein the method includes one or more processing devices performing operations comprising:
receiving, at a supervised classification engine, extracted linguistic features of an input text and a latent representation of the input text;
predicting, by the supervised classification engine, an affect characterization of the input text using the extracted linguistic features and the latent representation, wherein predicting the affect characterization comprises:
normalizing and concatenating a linguistic feature representation generated from the extracted linguistic features with the latent representation to generate an appended latent representation; and
identifying, by a gated convolutional encoder-decoder model, an affect label of the input text using the predicted affect characterization.