We examine the combination of pattern-based and distributional similarity for the induction of semantic categories. Pattern-based methods are precise and sparse while distributional methods have a higher recall. Given these particular properties we use the prediction of distributional methods as a back-off to pattern-based similarity. Since our pattern-based approach is embedded into a semi-sup...

This work explores computing distributional similarity between sub-parses, i.e., fragments of a parse tree, as an extension to general lexical distributional similarity techniques. In the same way that lexical distributional similarity is used to estimate lexical semantic similarity, we propose using distributional similarity between subparses to estimate the semantic similarity of phrases. Suc...

This paper describes and demonstrates a distributional semantic search service infrastructure for Linked Dataspaces. The center of the approach relies on the use of a distributional semantics infrastructure to provide semantic search and query services over data for users and applications, improving data accessibility over the Dataspace. By accessing the services through a REST API, users can s...

Using distributional analysis methods to compute semantic proximity links between words has become commonplace in NLP. This paper focuses on the issues of evaluating a distributional resource. We consider that setting up an evaluation procedure is a first step towards the characterization of the resource, and a way to improve its overall quality. We then propose a new protocol for in-text annot...