Improving Correlation with Human Judgments by Embedding Second Order Vectors with Semantic Similarity

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 paper 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.