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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 BASE Show 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; Yukawa, Haruki. - : arXiv, 2020 Abstract: Language-independent tokenisation (LIT) methods that do not require labelled language resources or lexicons have recently gained popularity because of their applicability in resource-poor languages. Moreover, they compactly represent a language using a fixed size vocabulary and can efficiently handle unseen or rare words. On the other hand, language-specific tokenisation (LST) methods have a long and established history, and are developed using carefully created lexicons and training resources. Unlike subtokens produced by LIT methods, LST methods produce valid morphological subwords. Despite the contrasting trade-offs between LIT vs. LST methods, their performance on downstream NLP tasks remain unclear. In this paper, we empirically compare the two approaches using semantic similarity measurement as an evaluation task across a diverse set of languages. Our experimental results covering eight languages show that LST consistently outperforms LIT when the vocabulary size is large, but LIT can produce ... : To appear in the 12th Language Resources and Evaluation (LREC 2020) Conference ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://arxiv.org/abs/2002.11004 https://dx.doi.org/10.48550/arxiv.2002.11004 BASE Hide 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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