Explorations of cross-disciplinary term similarity

Abstract

This paper presents some initial explorations into how to compute term similarity across different domains, or in the present case, scientific disciplines. In particular we explore the concepts of polysemy across disciplines, where the same term can have different meaning across different discipline. This can lead to confusion and/or erroneous query expansion, if the domain is not properly identified. Typical bag-of-words systems are not equipped to highlight such differences as terms would have a single representation. Identifying the synonymy of terms across different domains is also a difficult problem for typical bag-of-words systems, as they use surrounding words that will usually also be different across domains. Yet discovering such similarities across domains can support tasks such as literature discovery. We propose an approach that integrates knowledge based distances into a distributional semantics framework and demonstrate its efficiency on a hand-crafted dataset.