21 citations · 83 across the 20 of their papers we have counts for
35 papers
Learning to Borrow -- Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion
Huda Hakami, Mona Hakami, Angrosh Mandya +1
Prior work on integrating text corpora with knowledge graphs (KGs) to improve Knowledge Graph Embedding (KGE) have obtained good performance for entities that co-occur in sentences…
Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings
Danushka Bollegala
Given multiple source word embeddings learnt using diverse algorithms and lexical resources, meta word embedding learning methods attempt to learn more accurate and wide-coverage w…
Learning Sense-Specific Static Embeddings using Contextualised Word Embeddings as a Proxy
Yi Zhou, Danushka Bollegala
Contextualised word embeddings generated from Neural Language Models (NLMs), such as BERT, represent a word with a vector that considers the semantics of the target word as well it…
Semantically-Conditioned Negative Samples for Efficient Contrastive Learning
James O' Neill, Danushka Bollegala
Negative sampling is a limiting factor w.r.t. the generalization of metric-learned neural networks. We show that uniform negative sampling provides little information about the cla…
RelWalk A Latent Variable Model Approach to Knowledge Graph Embedding
Danushka Bollegala, Huda Hakami, Yuichi Yoshida +1
Embedding entities and relations of a knowledge graph in a low-dimensional space has shown impressive performance in predicting missing links between entities. Although progresses…
Dictionary-based Debiasing of Pre-trained Word Embeddings
Masahiro Kaneko, Danushka Bollegala
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 dictionar…