Abstract
In this paper, we study a new entity linking problem where both the entity mentions and the target entities are within a same social media platform. Compared with traditional entity linking problems that link mentions to a knowledge base, this new problem have less information about the target entities. However, if we can successfully link mentions to entities within a social media platform, we can improve a lot of applications such as comparative study in business intelligence and opinion leader finding. To study this problem, we constructed a dataset called Yelp-EL, where the business mentions in Yelp reviews are linked to their corresponding businesses on the platform. We conducted comprehensive experiments and analysis on this dataset with a learning to rank model that takes different types of features as input, as well as a few state-of-the-art entity linking approaches. Our experimental results show that two types of features that are not available in traditional entity linking: social features and location features, can be very helpful for this task.- Anthology ID:
- D18-1227
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2023–2032
- Language:
- URL:
- https://aclanthology.org/D18-1227
- DOI:
- 10.18653/v1/D18-1227
- Cite (ACL):
- Hongliang Dai, Yangqiu Song, Liwei Qiu, and Rijia Liu. 2018. Entity Linking within a Social Media Platform: A Case Study on Yelp. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2023–2032, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Entity Linking within a Social Media Platform: A Case Study on Yelp (Dai et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/D18-1227.pdf
- Code
- HKUST-KnowComp/ELWSMP