Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

9-2018

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.

Keywords

entity linking, food entities, query expansion

Discipline

Databases and Information Systems | Social Media

Research Areas

Data Science and Engineering

Publication

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases ECML-PKDD 2018, Dublin, Ireland, September 10-14

Volume

11053

First Page

169

Last Page

185

ISBN

9783030109967

Identifier

10.1007/978-3-030-10997-4_11

Publisher

Springer

City or Country

Cham

Copyright Owner and License

Authors

Additional URL

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

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