Semantic relatedness between words is a core concept in natural language processing.
While countless approaches have been proposed, measuring which one works best is still a …

Modern embedded applications typically integrate a multitude of functionalities with
potentially different criticality levels into a single system. Without appropriate preconditions …

In recent years word embedding/distributional semantic models evolved to become a
fundamental component in many natural language processing (NLP) architectures due to …

This paper provides a comparative analysis of the performance of four state-of-the-art
distributional semantic models (DSMs) over 11 languages, contrasting the native language …

Users combine attributes and types to describe and classify entities into categories. These
categories are fundamental for organising knowledge in a decentralised way acting as tags …

Distributional semantic models are strongly dependent on the size and the quality of the
reference corpora, which embeds the commonsense knowledge necessary to build …

In order to provide content-based search on image media, including images and video, they
are typically accessed based on manual or automatically assigned concepts or tags, or …

Dublin City University participated with a consortium of colleagues from NUI Galway and
Universitat Politecnica de Catalunya in two tasks in TRECVid 2016, Instance Search (INS) …

This paper presents SemantiGal, an online tool to explore vector models for Galician. It
includes a masked language modeling demonstrator using two monolingual BERT-based …

Different semantic interpretation tasks such as text entailment and question answering
require the classification of semantic relations between terms or entities within text …