Similarity Calculations of Academic Articles Using Topic Events and Domain Knowledge

Abstract

While studies investigating the semantic similarity among concepts, sentences and short text fragments have been fruitful, the problem of document-level semantic matching remains largely unexplored due to its complexity. In this paper, we explore the document-level semantic similarity issue in the academic literatures using an interpretable method. To integrally describe the semantics of an article, we construct a topic event model that utilizes multiple information facets, such as the study purposes, methodologies and domains. Furthermore, to better understand the documents and achieve a more accurate similarity comparison, we incorporate external knowledge into the topic event construction and similarity calculation. Our approach achieves significant improvements over state-of-the-art methods.