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Author: Avi Bleiweiss

Affiliation: BShalem Research, United States

ISBN: 978-989-758-275-2

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)

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Paper citation in several formats:
Bleiweiss, A. (2018). Machine Floriography: Sentiment-inspired Flower Predictions over Gated Recurrent Neural Networks.In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-275-2, pages 413-421. DOI: 10.5220/0006583204130421

@conference{icaart18,
author={Avi Bleiweiss.},
title={Machine Floriography: Sentiment-inspired Flower Predictions over Gated Recurrent Neural Networks},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2018},
pages={413-421},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006583204130421},
isbn={978-989-758-275-2},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Machine Floriography: Sentiment-inspired Flower Predictions over Gated Recurrent Neural Networks
SN - 978-989-758-275-2
AU - Bleiweiss, A.
PY - 2018
SP - 413
EP - 421
DO - 10.5220/0006583204130421

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