Danushka Bollegala
Danushka Bollegala
Professor, The University of Liverpool, DMML,
Verified email at - Homepage
Cited by
Cited by
Measuring semantic similarity between words using web search engines.
D Bollegala, Y Matsuo, M Ishizuka
www 7 (2007), 757-766, 2007
Cross-domain sentiment classification using a sentiment sensitive thesaurus
D Bollegala, D Weir, J Carroll
IEEE transactions on knowledge and data engineering 25 (8), 1719-1731, 2012
A web search engine-based approach to measure semantic similarity between words
D Bollegala, Y Matsuo, M Ishizuka
IEEE Transactions on knowledge and Data Engineering 23 (7), 977-990, 2010
Using multiple sources to construct a sentiment sensitive thesaurus for cross-domain sentiment classification
D Bollegala, D Weir, JA Carroll
Proceedings of the 49th annual meeting of the Association for Computational …, 2011
Social media and pharmacovigilance: a review of the opportunities and challenges
R Sloane, O Osanlou, D Lewis, D Bollegala, S Maskell, M Pirmohamed
British journal of clinical pharmacology 80 (4), 910-920, 2015
Relational duality: Unsupervised extraction of semantic relations between entities on the web
DT Bollegala, Y Matsuo, M Ishizuka
Proceedings of the 19th international conference on World wide web, 151-160, 2010
Gender-preserving debiasing for pre-trained word embeddings
M Kaneko, D Bollegala
arXiv preprint arXiv:1906.00742, 2019
Explanation in AI and law: Past, present and future
K Atkinson, T Bench-Capon, D Bollegala
Artificial Intelligence 289, 103387, 2020
Cross-domain sentiment classification using sentiment sensitive embeddings
D Bollegala, T Mu, JY Goulermas
IEEE Transactions on Knowledge and Data Engineering 28 (2), 398-410, 2015
A bottom-up approach to sentence ordering for multi-document summarization
D Bollegala, N Okazaki, M Ishizuka
Information processing & management 46 (1), 89-109, 2010
Frustratingly Easy Meta-Embedding--Computing Meta-Embeddings by Averaging Source Word Embeddings
J Coates, D Bollegala
arXiv preprint arXiv:1804.05262, 2018
Debiasing pre-trained contextualised embeddings
M Kaneko, D Bollegala
arXiv preprint arXiv:2101.09523, 2021
Measuring the similarity between implicit semantic relations from the web
DT Bollegala, Y Matsuo, M Ishizuka
Proceedings of the 18th international conference on World wide web, 651-660, 2009
Spinning multiple social networks for semantic web
Y Matsuo, M Hamasaki, Y Nakamura, T Nishimura, K Hasida, H Takeda, ...
Proceedings of the National Conference on Artificial Intelligence 21 (2), 1381, 2006
“touching to see” and “seeing to feel”: Robotic cross-modal sensory data generation for visual-tactile perception
JT Lee, D Bollegala, S Luo
2019 International Conference on Robotics and Automation (ICRA), 4276-4282, 2019
Joint word representation learning using a corpus and a semantic lexicon
D Bollegala, M Alsuhaibani, T Maehara, K Kawarabayashi
Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016
Unsupervised cross-domain word representation learning
D Bollegala, T Maehara, K Kawarabayashi
arXiv preprint arXiv:1505.07184, 2015
Automatic discovery of personal name aliases from the web
D Bollegala, Y Matsuo, M Ishizuka
IEEE Transactions on Knowledge and Data Engineering 23 (6), 831-844, 2010
DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach
Y Khemchandani, S O’Hagan, S Samanta, N Swainston, TJ Roberts, ...
Journal of cheminformatics 12, 1-17, 2020
Learning word meta-embeddings by autoencoding
D Bollegala, C Bao
Proceedings of the 27th international conference on computational …, 2018
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