World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
4351
World Ranking
12728
National Ranking
811

Overview

Danushka Bollegala is affiliated with the University of Liverpool in the United Kingdom. Their research primarily spans the field of Computer Science, with a strong focus on Artificial Intelligence. Additional subfields of study include Molecular Biology, Materials Chemistry, Cognitive Neuroscience, and Computer Vision and Pattern Recognition.

The scientist's work covers a wide range of topics within Artificial Intelligence and related areas. Key topics include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Speech and dialogue systems
  • Advanced Graph Neural Networks
  • Text and Document Classification Technologies
  • Text Readability and Simplification
  • Hate Speech and Cyberbullying Detection

Danushka Bollegala has contributed to academic literature through recent papers published in well-known venues, including:

  • "Explanation in AI and law: Past, present and future," 2020, Artificial Intelligence
  • "DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach," 2020, Journal of Cheminformatics
  • "Unmasking the Mask - Evaluating Social Biases in Masked Language Models," 2022, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Gender Bias in Masked Language Models for Multiple Languages," 2022, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  • "Random projections and kernelised leave one cluster out cross validation: universal baselines and evaluation tools for supervised machine learning of material properties," 2022, Digital Discovery

Their frequent publication venues include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • bioRxiv (Cold Spring Harbor Laboratory)
  • SSRN Electronic Journal
  • Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Danushka Bollegala collaborates regularly with several co-authors. Frequent collaborators include:

  • James O'Neill
  • Masahiro Kaneko
  • Yi Zhou
  • Naoaki Okazaki

Best Publications

  • Measuring Semantic Similarity between Words Using Web Search Engines

    Danushka Bollegala;Yutaka Matsuo;Mitsuru Ishizuka

  • Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus

    D. Bollegala;D. Weir;J. Carroll

  • A Web Search Engine-Based Approach to Measure Semantic Similarity between Words

    D. Bollegala;Y. Matsuo;M. Ishizuka

  • Using Multiple Sources to Construct a Sentiment Sensitive Thesaurus for Cross-Domain Sentiment Classification

    Danushka Bollegala;David Weir;John Carroll

  • Social media and pharmacovigilance: A review of the opportunities and challenges

    Richard Sloane;Orod Osanlou;Orod Osanlou;David Lewis;Danushka Bollegala

  • Relational duality: unsupervised extraction of semantic relations between entities on the web

    Danushka Tarupathi Bollegala;Yutaka Matsuo;Mitsuru Ishizuka

  • Explanation in AI and law: Past, present and future

    Katie Atkinson;Trevor J. M. Bench-Capon;Danushka Bollegala

  • Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings

    Danushka Bollegala;Tingting Mu;John Yannis Goulermas

  • A bottom-up approach to sentence ordering for multi-document summarization

    Danushka Bollegala;Naoaki Okazaki;Mitsuru Ishizuka

  • Gender-preserving Debiasing for Pre-trained Word Embeddings

    Masahiro Kaneko;Danushka Bollegala

  • Frustratingly Easy Meta-Embedding -- Computing Meta-Embeddings by Averaging Source Word Embeddings

    Joshua Coates;Danushka Bollegala

  • Measuring the similarity between implicit semantic relations from the web

    Danushka T. Bollegala;Yutaka Matsuo;Mitsuru Ishizuka

  • Debiasing Pre-trained Contextualised Embeddings.

    Masahiro Kaneko;Danushka Bollegala

  • Spinning multiple social networks for semantic web

    Yutaka Matsuo;Masahiro Hamasaki;Yoshiyuki Nakamura;Takuichi Nishimura

  • Unsupervised Cross-Domain Word Representation Learning

    Danushka Bollegala;Takanori Maehara;Ken-ichi Kawarabayashi

  • Automatic Discovery of Personal Name Aliases from the Web

    D Bollegala;Y Matsuo;M Ishizuka

  • “Touching to See” and “Seeing to Feel”: Robotic Cross-modal Sensory Data Generation for Visual-Tactile Perception

    Jet-Tsyn Lee;Danushka Bollegala;Shan Luo

  • DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach.

    Yash Khemchandani;Yash Khemchandani;Stephen O’Hagan;Soumitra Samanta;Neil Swainston

  • Joint word representation learning using a corpus and a semantic lexicon

    Danushka Bollegala;Alsuhaibani Mohammed;Takanori Maehara;Ken-ichi Kawarabayashi

  • Disambiguating Personal Names on the Web using Automatically Extracted Key Phrases

    Danushka Bollegala;Yutaka Matsuo;Mitsuru Ishizuka

  • A Relational Model of Semantic Similarity between Words using Automatically Extracted Lexical Pattern Clusters from the Web

    Danushka Bollegala;Yutaka Matsuo;Mitsuru Ishizuka

  • Learning Word Meta-Embeddings by Autoencoding

    Danushka Bollegala;Cong Bao

  • An adaptive differential evolution algorithm

    Nasimul Noman;Danushka Bollegala;Hitoshi Iba

  • Spatio-temporal Attention Model for Tactile Texture Recognition

    Guanqun Cao;Yi Zhou;Danushka Bollegala;Shan Luo

  • "Touching to See" and "Seeing to Feel": Robotic Cross-modal SensoryData Generation for Visual-Tactile Perception

    Jet-Tsyn Lee;Danushka Bollegala;Shan Luo

Frequent Co-Authors

Mitsuru Ishizuka
Mitsuru Ishizuka University of Tokyo
Yutaka Matsuo
Yutaka Matsuo University of Tokyo
Frans Coenen
Frans Coenen University of Liverpool
Ken-ichi Kawarabayashi
Ken-ichi Kawarabayashi National Institute of Informatics
Naoaki Okazaki
Naoaki Okazaki Tokyo Institute of Technology
Hitoshi Iba
Hitoshi Iba University of Tokyo
Simon Parsons
Simon Parsons University of Lincoln
Kiyoharu Aizawa
Kiyoharu Aizawa University of Tokyo
David J. Weir
David J. Weir Helsinki Institute of Physics
Douglas B. Kell
Douglas B. Kell University of Liverpool

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