Article

Implicit Linking of Food Entities in Social Media

Authors:

Ee-Peng LimAuthors Info & Claims

Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part III

Pages 169 - 185

https://doi.org/10.1007/978-3-030-10997-4_11

Published: 18 January 2019 Publication History

Abstract

Dining is an important part in people’s lives and this explains why food-related microblogs and reviews are popular in social media. Identifying food entities in food-related posts is important to food lover profiling and food (or restaurant) recommendations. In this work, we conduct Implicit Entity Linking (IEL) to link food-related posts to food entities in a knowledge base. In IEL, we link posts even if they do not contain explicit entity mentions. We first show empirically that food venues are entity-focused and associated with a limited number of food entities each. Hence same-venue posts are likely to share common food entities. Drawing from these findings, we propose an IEL model which incorporates venue-based query expansion of test posts and venue-based prior distributions over entities. In addition, our model assigns larger weights to words that are more indicative of entities. Our experiments on Instagram captions and food reviews shows our proposed model to outperform competitive baselines.

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Cited By

Feher DIbrahim FCheng ZSchlegel VMaidment TBagshaw JBatista-Navarro R(2023)Few-shot entity linking of food namesInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10346360:5Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.ipm.2023.103463

Index Terms

Implicit Linking of Food Entities in Social Media
1. Computing methodologies
  1. Artificial intelligence
2. Information systems
  1. Information retrieval
  2. Information systems applications

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Guide Proceedings

Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part III

Sep 2018

680 pages

ISBN:978-3-030-10996-7

DOI:10.1007/978-3-030-10997-4

Editors:
Ulf Brefeld
Leuphana University, Lüneburg, Germany
,
Edward Curry
National University of Ireland, Galway, Ireland
,
Elizabeth Daly
IBM Research - Ireland, Dublin, Ireland
,
Brian MacNamee
University College Dublin, Dublin, Ireland
,
Alice Marascu
Nokia (Ireland), Dublin, Ireland
,
Fabio Pinelli
Vodafone, Milan, Italy
,
Michele Berlingerio
IBM Research - Ireland, Dublin, Ireland
,
Neil Hurley
University College Dublin, Dublin, Ireland

© Springer Nature Switzerland AG 2019.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 18 January 2019

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Cited By

Feher DIbrahim FCheng ZSchlegel VMaidment TBagshaw JBatista-Navarro R(2023)Few-shot entity linking of food namesInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10346360:5Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.ipm.2023.103463

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