Ontology Enhanced Claim Detection
This work addresses claim detection for small datasets, but it is incremental as it builds on existing methods by adding domain-specific features.
The authors tackled claim detection in small, unbalanced datasets by fusing ontology embeddings with BERT sentence embeddings, achieving the best results on ClaimBuster and NewsClaims datasets compared to other models.
We propose an ontology enhanced model for sentence based claim detection. We fused ontology embeddings from a knowledge base with BERT sentence embeddings to perform claim detection for the ClaimBuster and the NewsClaims datasets. Our ontology enhanced approach showed the best results with these small-sized unbalanced datasets, compared to other statistical and neural machine learning models. The experiments demonstrate that adding domain specific features (either trained word embeddings or knowledge graph metadata) can improve traditional ML methods. In addition, adding domain knowledge in the form of ontology embeddings helps avoid the bias encountered in neural network based models, for example the pure BERT model bias towards larger classes in our small corpus.