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  1. Proceedings of the 9th Workshop on Multimedia for Cooking and Eating Activities in conjunction with The 2017 International Joint Conference on Artificial Intelligence (CEA2017)
  2. Multilingualization of Restaurant Menu by Analogical Description
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Estimation of the Attractiveness of Food Photography Focusing on Main Ingredients
Concurrence of Word Concepts in Cooking Recipe Search
Approaches to Food/Non-food Image Classification Using Deep Learning in Cookpad
A Comparison of Cooking Recipe Named Entities between Japanese and English
Discovering New Creative Mixtures of Cooking Ingredients
Learning Food Appearance by a Supervision with Recipe Text
Multilingualization of Restaurant Menu by Analogical Description
Prediction of Individual Eating Habits Using Short-Term Food Recording
Calculating Cooking Recipe's Difficulty based on Cooking Activities
Method for Detecting Near-duplicate Recipe Creators Based on Cooking Instructions and Food Images

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Multilingualization of Restaurant Menu by Analogical Description

Content Provider ACM Digital Library
Author Hirota, Masaharu Endo, Masaki Kato, Daiju Ishikawa, Hiroshi Nobumoto, Kensuke
Abstract As described in this paper, we propose a new analogical description for a restaurant menu and a method to generate it automatically. A description for a dish of the menu consists of a similar dish and differences between those dishes, such as 'Okonomiyaki = Korean pancake - leek + cabbage'. To extract a similar dish and differences for the description, we analyze recipe data of cooking sites. We specifically examine six elements as differences of dishes and its similar dishes (e.g., foods and cooking-related verbs). We demonstrate that our proposed description is expressible as a restaurant menu.
Starting Page 13
Ending Page 18
Page Count 6
File Format PDF
ISBN 9781450352673
DOI 10.1145/3106668.3106671
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2017-08-20
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Recipe site Similarity dish Internationalization
Content Type Text
Resource Type Article
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