Speaker: Ted Pedersen
, University of Minnesota, Duluth
Title: Improving Relatedness Measurements of Biomedical Concepts by Embedding Second-Order Vectors with Similarity Measurements (or: eating your own tail is good for you)
Vector space methods that measure semantic similarity and relatedness often rely on distributional information such as co-occurrence frequencies or statistical measures of association to weight the importance of particular co-occurrences. In this work we extend these methods by embedding a measure of semantic similarity based on a human curated taxonomy into a second-order vector representation. This results in a measure of semantic relatedness that combines both the contextual information available in a corpus-based vector space representation with the semantic knowledge found in a biomedical ontology. Our results show that embedding semantic semantic similarity into a second-order co-occurrence matrix improves correlation with human judgments for both similarity and relatedness.
(Joint work with Bridget McInnes, Virginia Commonwealth University)