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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. - : 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 Abstract: Knowledge Graph Embedding methods learn low-dimensional representations for entities and relations in knowledge graphs, which can be used to infer previously unknown relations between pairs of entities in the knowledge graph. This is particularly useful for expanding otherwise sparse knowledge graphs. However, the relation types that can be predicted using knowledge graph embeddings are confined to the set of relations that already exists in the KG. Often the set of relations that exist between two entities are not independent, and it is possible to predict what other relations are likely to exist between two entities by composing the embeddings of the relations in which each entity participates. We introduce relation composition as the task of inferring embeddings for unseen relations by combining existing relations in a knowledge graph. Specifically, we propose a supervised method to compose relational embeddings for novel relations using pre-trained relation embeddings for existing relations. Our experimental results on a previously proposed benchmark dataset for relation composition ranking and triple classification show that the proposed supervised relation composition method outperforms several unsupervised relation composition methods. URL: http://livrepository.liverpool.ac.uk/3099008/1/Chen_PACLING_2019%20%281%29.pdf https://link.springer.com/chapter/10.1007%2F978-981-15-6168-9_5 http://livrepository.liverpool.ac.uk/3099008/ BASE Hide 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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