The present study is part of a larger research project with the goal of developing tools for
large-scale corpus-based semantic analyses. One such tool involves representing semantic …

The present study is part of a larger research project developing computational tools for
large-scale corpus-based semantic analyses. One such tool represents semantic structure …

This paper presents a multilingual study of word meaning representations in context. We
assess the ability of both static and contextualized models to adequately represent different …

In this chapter, we present a visual analytics approach to the corpus-based study of lexical
semantics. The approach combines distributional semantic modelling of word tokens with an …

ABSTRACT Within Computational Linguistics, distributional models of semantics have
become the mainstay of large-scale modelling of lexical semantics. Distributional modelling …

Some words are more contentful than others: for instance, make is intuitively more general
than produce and fifteen is more 'precise'than a group. In this paper, we propose to measure …

This paper demonstrates how token-level Word Space Models (a distributional semantic
technique that was originally developed in statistical natural language processing) can be …

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 …

Traditional vector-based models use word co-occurrence counts from large corpora to
represent lexical meaning. In this paper we present a novel approach for constructing …

In statistical NLP, Semantic Vector Spaces (SVS) are the standard technique for the
automatic modeling of lexical semantics. However, it is largely unclear how these black-box …