All Outputs (3)

A mixture-of-experts model for learning multi-facet entity embeddings (2020)
Conference Proceeding
Alshaikh, R., Bouraoui, Z., Jeawak, S., & Schockaert, S. (2020). A mixture-of-experts model for learning multi-facet entity embeddings. In Proceedings of the 28th International Conference on Computational Linguistics (5124-5135)

Various methods have already been proposed for learning entity embeddings from text descriptions. Such embeddings are commonly used for inferring properties of entities, for recommendation and entity-oriented search, and for injecting background know... Read More about A mixture-of-experts model for learning multi-facet entity embeddings.

Cardiff University at SemEval-2020 Task 6: Fine-tuning BERT for Domain-Specific Definition Classification (2020)
Conference Proceeding
Jeawak, S. S., Espinosa-Anke, L., & Schockaert, S. (2020). Cardiff University at SemEval-2020 Task 6: Fine-tuning BERT for Domain-Specific Definition Classification. In Proceedings of the Fourteenth Workshop on Semantic Evaluation (361-366)

We describe the system submitted to SemEval-2020 Task 6, Subtask 1. The aim of this subtask is to predict whether a given sentence contains a definition or not. Unsurprisingly, we found that strong results can be achieved by fine-tuning a pre-trained... Read More about Cardiff University at SemEval-2020 Task 6: Fine-tuning BERT for Domain-Specific Definition Classification.

Predicting the environment from social media: A collective classification approach (2020)
Journal Article
Jeawak, S. S., Jones, C. B., & Schockaert, S. (2020). Predicting the environment from social media: A collective classification approach. Computers, Environment and Urban Systems, 82, https://doi.org/10.1016/j.compenvurbsys.2020.101487

We propose a method which uses Flickr tags to predict a wide variety of environmental features, such as climate data, land cover categories, species occurrence, and human assessments of scenicness. The role of Flickr tags in our method is two-fold. F... Read More about Predicting the environment from social media: A collective classification approach.