Over the past 15 years, a range of methods have been developed that are able to learn
human-like estimates of the semantic relatedness between terms from the way in which …

Distributional approaches to meaning acquisition utilize distributional properties of linguistic
entities as the building blocks of semantics. In doing so, they rely fundamentally on a set of …

Semantic taxonomies such as WordNet provide a rich source of knowl-edge for natural
language processing applications, but are expensive to build, maintain, and extend …

Traditionally, vector-based semantic space models use word co-occurrence counts from
large corpora to represent lexical meaning. In this article we present a novel framework for …

The need for the establishment of evaluation methods that can measure respective
improvements or degradations of ontological models-eg yielded by a precursory ontology …

The rapid advancement in the theoretical understanding of statistical and machine learning
methods for semisupervised learning has made it difficult for nonspecialists to keep up to …

Lexical-semantic resources, including thesauri and WORDNET, have been successfully
incorporated into a wide range of applications in Natural Language Processing. However …

After the vision of the Semantic Web was broadcasted at the turn of the millennium, ontology
became a synonym for the solution to many problems concerning the fact that computers do …

This paper presents a constructioninspecific model of multiword expression decomposability
based on latent semantic analysis. We use latent semantic analysis to determine the …

Although many algorithms have been developed to harvest lexical resources, few organize
the mined terms into taxonomies. We propose (1) a semi-supervised algorithm that uses a …