Implicit linking of food entities in social media

• Main Authors: CHONG, Wen Haw, LIM, Ee Peng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: entity linking; food entities; query expansion; Databases and Information Systems; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/4322; https://ink.library.smu.edu.sg/context/sis_research/article/5325/viewcontent/Food_Social_Media_2018_ECML_PKDD.pdf
• Institution: Singapore Management University

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.