Author:
Avi Bleiweiss
Affiliation:
BShalem Research, United States
Keyword(s):
Language of Flowers, Gated Recurrent Neural Networks, Machine Translation, Softmax Regression.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Evolutionary Computing
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Knowledge Discovery and Information Retrieval
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Machine Learning
;
Methodologies and Methods
;
Natural Language Processing
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
;
Theory and Methods
Abstract:
The design of a flower bouquet often comprises a manual step of plant selection that follows an artistic style
arrangement. Floral choices for a collection are typically founded on visual aesthetic principles that include
shape, line, and color of petals. In this paper, we propose a novel framework that instead classifies sentences
that describe sentiments and emotions typically conveyed by flowers, and predicts the bouquet content implicitly.
Our work exploits the figurative Language of Flowers that formalizes an expandable list of translation
records, each mapping a short-text sentiment sequence to a unique flower type we identify with the bouquet
center-of-interest. Records are represented as word embeddings we feed into a gated recurrent neural-network,
and a discriminative decoder follows to maximize the score of the lead flower and rank complementary flower
types based on their posterior probabilities. Already normalized, these scores directly shape the mix weights
in the final
arrangement and support our intuition of a naturally formed bouquet. Our quantitative evaluation
reviews both stand-alone and baseline comparative results.
(More)