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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 Hits 1 – 20 of 44 1 Learning to Borrow -- Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion ... Hakami, Huda; Hakami, Mona; Mandya, Angrosh. - : arXiv, 2022 BASE Show details 2 Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings ... Bollegala, Danushka. - : arXiv, 2022 BASE Show details 3 Sense Embeddings are also Biased--Evaluating Social Biases in Static and Contextualised Sense Embeddings Kaneko, Masahiro; Zhou, Yi; Bollegala, Danushka. - 2022 BASE Show details 4 I Wish I Would Have Loved This One, But I Didn't -- A Multilingual Dataset for Counterfactual Detection in Product Reviews ... O'Neill, James; Rozenshtein, Polina; Kiryo, Ryuichi. - : arXiv, 2021 BASE Show details 5 Detect and Classify – Joint Span Detection and Classification for Health Outcomes ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Abaho, Micheal; Bollegala, Danushka. - : Underline Science Inc., 2021 BASE Show details 6 Unsupervised Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidance ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bollegala, Danushka; Isonuma, Masaru; Mori, Junichiro; Sakata, Ichiro. - : Underline Science Inc., 2021 Abstract: This paper presents a novel unsupervised abstractive summarization method for opinionated texts. While the basic variational autoencoder-based models assume a unimodal Gaussian prior for the latent code of sentences, we alternate it with a recursive Gaussian mixture, where each mixture component corresponds to the latent code of a topic sentence and is mixed by a tree-structured topic distribution. By decoding each Gaussian component, we generate sentences with tree-structured topic guidance, where the root sentence conveys generic content, and the leaf sentences describe specific topics. Experimental results demonstrate that the generated topic sentences are appropriate as a summary of opinionated texts, which are more informative and cover more input contents than those generated by the recent unsupervised summarization model (Bražinskas et al., 2020). Furthermore, we demonstrate that the variance of latent Gaussians represents the granularity of sentences, analogous to Gaussian word embedding (Vilnis and ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Text Summarization URL: https://dx.doi.org/10.48448/eeqw-5327 https://underline.io/lecture/38985-unsupervised-abstractive-opinion-summarization-by-generating-sentences-with-tree-structured-topic-guidance BASE Hide details 7 Fine-Tuning Word Embeddings for Hierarchical Representation of Data Using a Corpus and a Knowledge Base for Various Machine Learning Applications Alsuhaibani, Mohammed; Bollegala, Danushka In: Comput Math Methods Med (2021) BASE Show details 8 RelWalk - A Latent Variable Model Approach to Knowledge Graph Embedding. Bollegala, Danushka; Kawarabayashi, Ken-ichi; Yoshida, Yuichi. - : Association for Computational Linguistics, 2021 BASE Show details 9 Dictionary-based Debiasing of Pre-trained Word Embeddings. Bollegala, Danushka; Kaneko, Masahiro. - : Association for Computational Linguistics, 2021 BASE Show details 10 Unsupervised Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidance Sakata, Ichiro; Mori, Junichiro; Bollegala, Danushka. - : Massachusetts Institute of Technology Press, 2021 BASE Show details 11 Unsupervised Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidance Bollegala, Danushka; Isonuma, Masaru; Mori, Junichiro. - 2021 BASE Show details 12 Debiasing Pre-trained Contextualised Embeddings. Kaneko, Masahiro; Bollegala, Danushka. - : Association for Computational Linguistics, 2021 BASE Show details 13 Autoencoding Improves Pre-trained Word Embeddings ... Kaneko, Masahiro; Bollegala, Danushka. - : arXiv, 2020 BASE Show details 14 Autoencoding Improves Pre-trained Word Embeddings ... The 28th International Conference on Computational Linguistics 2020; Bollegala, Danushka; Kaneko, Masahiro. - : Underline Science Inc., 2020 BASE Show details 15 Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction ... The 28th International Conference on Computational Linguistics 2020; Bollegala, Danushka; Coenen, Frans. - : Underline Science Inc., 2020 BASE Show details 16 Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction ... Bollegala, Danushka; Kiryo, Ryuichi; Tsujino, Kosuke. - : arXiv, 2020 BASE Show details 17 Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction. Mandya, Angrosh; Coenen, Frans; Bollegala, Danushka. - : International Committee on Computational Linguistics, 2020 BASE Show details 18 Multi-Source Attention for Unsupervised Domain Adaptation. Bollegala, Danushka; Cui, Xia. - : Association for Computational Linguistics, 2020 BASE Show details 19 Learning to Compose Relational Embeddings in Knowledge Graphs Hakami, Huda; Chen, Wenye; Bollegala, Danushka. - : Springer Singapore, 2020 BASE Show details 20 Tree-Structured Neural Topic Model Isonuma, Masaru; Mori, Junichiro; Bollegala, Danushka. - 2020 BASE Show details Page: 1 2 3

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