Human similarity and relatedness judgements between concepts underlie most of cognitive
capabilities, such as categorisation, memory, decision-making and reasoning. For this …

Over the past years, distributed semantic representations have proved to be effective and
flexible keepers of prior knowledge to be integrated into downstream applications. This …

Although deep learning breakthroughs in NLP are based on learning distributed word
representations by neural language models, these methods suffer from a classic drawback …

While GPGPU stands for general-purpose computation on graphics processing units, the
lack of explicit support for inter-block communication on the GPU arguably hampers its …

Abstract Knowledge resources, eg knowledge graphs, which formally represent essential
semantics and information for logic inference and reasoning, can compensate for the …

Semantic specialization of distributional word vectors, referred to as retrofitting, is a process
of fine-tuning word vectors using external lexical knowledge in order to better embed some …

In industrial systems, textual failure records note the failure mechanisms, the parts involved,
and the failure symptoms; these records guide fault analysis and repair. However, case …

Word embeddings trained on large corpora have shown to encode high levels of unfair
discriminatory gender, racial, religious and ethnic biases. In contrast, human-written …

Distributed word embeddings have shown superior performances in numerous Natural
Language Processing (NLP) tasks. However, their performances vary significantly across …

We propose bidirectional imparting or BiImp, a generalized method for aligning embedding
dimensions with concepts during the embedding learning phase. While preserving the …