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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 – 8 of 8 1 Learning Disentangled Representations of Negation and Uncertainty ... Vasilakes, Jake; Zerva, Chrysoula; Miwa, Makoto. - : arXiv, 2022 BASE Show details 2 A Neural Edge-Editing Approach for Document-Level Relation Graph Extraction ... Makino, Kohei; Miwa, Makoto; Sasaki, Yutaka. - : arXiv, 2021 BASE Show details 3 A Neural Edge-Editing Approach for Document-Level Relation Graph Extraction ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Makino, Kohei; Miwa, Makoto. - : Underline Science Inc., 2021 BASE Show details 4 BioVAE: a pre-trained latent variable language model for biomedical text mining Trieu, Hai-Long; Miwa, Makoto; Ananiadou, Sophia In: Bioinformatics (2021) BASE Show details 5 The well-formedness and the ill-formedness of the JSL type III syllabe Hara, Daiuske; Miwa, Makoto In: CLS 55, 2019 : proceedings of the fifty-fifth annual meeting of the Chicago Linguistic Society (2020), S. 205-219 Leibniz-Zentrum Allgemeine Sprachwissenschaft Show details 6 Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network ... Sahu, Sunil Kumar; Christopoulou, Fenia; Miwa, Makoto. - : arXiv, 2019 BASE Show details 7 End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures ... Miwa, Makoto; Bansal, Mohit. - : arXiv, 2016 Abstract: We present a novel end-to-end neural model to extract entities and relations between them. Our recurrent neural network based model captures both word sequence and dependency tree substructure information by stacking bidirectional tree-structured LSTM-RNNs on bidirectional sequential LSTM-RNNs. This allows our model to jointly represent both entities and relations with shared parameters in a single model. We further encourage detection of entities during training and use of entity information in relation extraction via entity pretraining and scheduled sampling. Our model improves over the state-of-the-art feature-based model on end-to-end relation extraction, achieving 12.1% and 5.7% relative error reductions in F1-score on ACE2005 and ACE2004, respectively. We also show that our LSTM-RNN based model compares favorably to the state-of-the-art CNN based model (in F1-score) on nominal relation classification (SemEval-2010 Task 8). Finally, we present an extensive ablation analysis of several model components. ... : Accepted for publication at the Association for Computational Linguistics (ACL), 2016. 13 pages, 1 figure, 6 tables ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://dx.doi.org/10.48550/arxiv.1601.00770 https://arxiv.org/abs/1601.00770 BASE Hide details 8 Task-Oriented Learning of Word Embeddings for Semantic Relation Classification ... Hashimoto, Kazuma; Stenetorp, Pontus; Miwa, Makoto. - : arXiv, 2015 BASE Show details

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