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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 – 6 of 6 1 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 2 Think Globally, Embed Locally --- Locally Linear Meta-embedding of Words ... Bollegala, Danushka; Hayashi, Kohei; Kawarabayashi, Ken-ichi. - : arXiv, 2017 BASE Show details 3 Joint Word Representation Learning Using a Corpus and a Semantic Lexicon. Bollegala, Danushka; Alsuhaibani, Mohammed; Maehara, Takanori. - : AAAI Press, 2016 BASE Show details 4 Unsupervised Cross-Domain Word Representation Learning ... Bollegala, Danushka; Maehara, Takanori; Kawarabayashi, Ken-ichi. - : arXiv, 2015 BASE Show details 5 Joint Word Representation Learning using a Corpus and a Semantic Lexicon ... Bollegala, Danushka; Mohammed, Alsuhaibani; Maehara, Takanori; Kawarabayashi, Ken-ichi. - : arXiv, 2015 Abstract: Methods for learning word representations using large text corpora have received much attention lately due to their impressive performance in numerous natural language processing (NLP) tasks such as, semantic similarity measurement, and word analogy detection. Despite their success, these data-driven word representation learning methods do not consider the rich semantic relational structure between words in a co-occurring context. On the other hand, already much manual effort has gone into the construction of semantic lexicons such as the WordNet that represent the meanings of words by defining the various relationships that exist among the words in a language. We consider the question, can we improve the word representations learnt using a corpora by integrating the knowledge from semantic lexicons?. For this purpose, we propose a joint word representation learning method that simultaneously predicts the co-occurrences of two words in a sentence subject to the relational constrains given by the semantic ... : Accepted to AAAI-2016 ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1511.06438 https://dx.doi.org/10.48550/arxiv.1511.06438 BASE Hide details 6 Embedding Semantic Relations into Word Representations ... Bollegala, Danushka; Maehara, Takanori; Kawarabayashi, Ken-ichi. - : arXiv, 2015 BASE Show details

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