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 this paper we perform a comparative analysis of three models for a feature representation
of text documents in the context of document classification. In particular, we consider the …

Text entailment, the task of determining whether a piece of text logically follows from another
piece of text, has become an important component for many natural language processing …

The task of answering natural language questions over RDF data has received wide interest
in recent years, in particular in the context of the series of QALD benchmarks. The task …

This work describes SemR-11, a multi-lingual dataset for evaluating semantic similarity and
relatedness for 11 languages (German, French, Russian, Italian, Dutch, Chinese …

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 focuses on aspect extraction which is a sub-task of Aspect-based Sentiment
Analysis. The goal is to report an extraction method of financial aspects in microblog …

Due to recent advances in natural language processing (NLP), a new generation of digital
learning support systems is emerging, which make it possible to analyse the writing quality …

A distributional semantics model—also known as word embeddings—is a major asset for
any language as the research results reported in the literature have consistently shown that …