4 papers
A Survey on Word Meta-Embedding Learning
Danushka Bollegala, James O'Neill
Meta-embedding (ME) learning is an emerging approach that attempts to learn more accurate word embeddings given existing (source) word embeddings as the sole input. Due to their ab…
Analysing Dropout and Compounding Errors in Neural Language Models
James O' Neill, Danushka Bollegala
This paper carries out an empirical analysis of various dropout techniques for language modelling, such as Bernoulli dropout, Gaussian dropout, Curriculum Dropout, Variational Drop…
Angular-Based Word Meta-Embedding Learning
James O' Neill, Danushka Bollegala
Ensembling word embeddings to improve distributed word representations has shown good success for natural language processing tasks in recent years. These approaches either carry o…
Automatic Taxonomy Generation - A Use-Case in the Legal Domain
Cécile Robin, James O'Neill, Paul Buitelaar
A key challenge in the legal domain is the adaptation and representation of the legal knowledge expressed through texts, in order for legal practitioners and researchers to access…