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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 Hits 1 – 13 of 13 1 Autoencoding Improves Pre-trained Word Embeddings ... Kaneko, Masahiro; Bollegala, Danushka. - : arXiv, 2020 Abstract: Prior work investigating the geometry of pre-trained word embeddings have shown that word embeddings to be distributed in a narrow cone and by centering and projecting using principal component vectors one can increase the accuracy of a given set of pre-trained word embeddings. However, theoretically, this post-processing step is equivalent to applying a linear autoencoder to minimise the squared l2 reconstruction error. This result contradicts prior work (Mu and Viswanath, 2018) that proposed to remove the top principal components from pre-trained embeddings. We experimentally verify our theoretical claims and show that retaining the top principal components is indeed useful for improving pre-trained word embeddings, without requiring access to additional linguistic resources or labelled data. ... : COLING 2020 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2010.13094 https://arxiv.org/abs/2010.13094 BASE Hide details 2 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 3 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 4 Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction ... Bollegala, Danushka; Kiryo, Ryuichi; Tsujino, Kosuke. - : arXiv, 2020 BASE Show details 5 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 6 Multi-Source Attention for Unsupervised Domain Adaptation. Bollegala, Danushka; Cui, Xia. - : Association for Computational Linguistics, 2020 BASE Show details 7 Learning to Compose Relational Embeddings in Knowledge Graphs Hakami, Huda; Chen, Wenye; Bollegala, Danushka. - : Springer Singapore, 2020 BASE Show details 8 Tree-Structured Neural Topic Model Isonuma, Masaru; Mori, Junichiro; Bollegala, Danushka. - 2020 BASE Show details 9 A Pilot Study on Argument Simplification in Stance-Based Opinions Bollegala, Danushka; Rajendran, Pavithra; Parsons, Simon. - : Springer Singapore, 2020 BASE Show details 10 Joint Approaches for Learning Word Representations from Text Corpora and Knowledge Bases Alsuhaibani, Mohammed. - 2020 BASE Show details 11 Autoencoding Improves Pre-trained Word Embeddings. Kaneko, Masahiro; Bollegala, Danushka. - : International Committee on Computational Linguistics, 2020 BASE Show details 12 Context-Guided Self-supervised Relation Embeddings Hakami, Huda; Bollegala, Danushka. - : Springer Singapore, 2020 BASE Show details 13 A Study on Learning Representations for Relations Between Words Hakami, Huda. - 2020 BASE Show details

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