2024/02/19 by Zehra Melce Hüsünbeyi, Hüsünbeyi, Zehra Melce, Tatjana Scheffler +1
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Network Security and Intrusion Detection
paper · pdf · doi:10.48550/arxiv.2402.12282
openalex publication_date 2024/02/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.