Ontology Enhanced Claim Detection

• URL: http://arxiv.org/abs/2402.12282v1
• Date: Mon, 19 Feb 2024 16:50:58 GMT
• Title: Ontology Enhanced Claim Detection
• Authors: Zehra Melce H\"us\"unbeyi and Tatjana Scheffler
• Abstract summary: We propose an ontology enhanced model for sentence based claim detection. We fused knowledge base with BERT sentence embeddings to perform claim detection for the ClaimBuster and the NewsClaims datasets. Our approach showed the best results with these small-sized unbalanced datasets, compared to other statistical and neural machine learning models.
• Score: 1.0878040851637998
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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