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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 – 18 of 18 1 PhoMT: A High-Quality and Large-Scale Benchmark Dataset for Vietnamese-English Machine Translation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Doan, Long; Hoang, Thai. - : Underline Science Inc., 2021 BASE Show details 2 BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese ... Tran, Nguyen Luong; Le, Duong Minh; Nguyen, Dat Quoc. - : arXiv, 2021 BASE Show details 3 ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents He, Jiayuan; Nguyen, Dat Quoc; Akhondi, Saber A.... In: Front Res Metr Anal (2021) BASE Show details 4 PhoBERT: Pre-trained language models for Vietnamese ... Nguyen, Dat Quoc; Nguyen, Anh Tuan. - : arXiv, 2020 BASE Show details 5 A Pilot Study of Text-to-SQL Semantic Parsing for Vietnamese ... Nguyen, Anh Tuan; Dao, Mai Hoang; Nguyen, Dat Quoc. - : arXiv, 2020 BASE Show details 6 A survey of embedding models of entities and relationships for knowledge graph completion ... The 28th International Conference on Computational Linguistics 2020; Nguyen, Dat Quoc. - : Underline Science Inc., 2020 BASE Show details 7 Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings ... Zhai, Zenan; Nguyen, Dat Quoc; Akhondi, Saber A.. - : arXiv, 2019 BASE Show details 8 From POS tagging to dependency parsing for biomedical event extraction Nguyen, Dat Quoc; Verspoor, Karin. - : BioMed Central, 2019 BASE Show details 9 CoNLL 2018 Shared Task System Outputs Zeman, Daniel; Potthast, Martin; Duthoo, Elie. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2018 BASE Show details 10 An improved neural network model for joint POS tagging and dependency parsing ... Nguyen, Dat Quoc; Verspoor, Karin. - : arXiv, 2018 BASE Show details 11 VnCoreNLP: A Vietnamese Natural Language Processing Toolkit ... Vu, Thanh; Nguyen, Dat Quoc; Nguyen, Dai Quoc. - : arXiv, 2018 BASE Show details 12 NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter ... Vu, Thanh; Nguyen, Dat Quoc; Vu, Xuan-Son. - : arXiv, 2018 BASE Show details 13 CoNLL 2017 Shared Task System Outputs Zeman, Daniel; Potthast, Martin; Straka, Milan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017 BASE Show details 14 Sequence to Sequence Learning for Event Prediction ... Nguyen, Dai Quoc; Nguyen, Dat Quoc; Chu, Cuong Xuan. - : arXiv, 2017 BASE Show details 15 Modeling topics and knowledge bases with vector representations Nguyen, Dat Quoc. - : Sydney, Australia : Macquarie University, 2017 BASE Show details 16 From Word Segmentation to POS Tagging for Vietnamese ... Nguyen, Dat Quoc; Vu, Thanh; Nguyen, Dai Quoc. - : arXiv, 2017 BASE Show details 17 STransE: a novel embedding model of entities and relationships in knowledge bases ... Nguyen, Dat Quoc; Sirts, Kairit; Qu, Lizhen; Johnson, Mark. - : arXiv, 2016 Abstract: Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform link prediction or knowledge base completion, i.e., predict whether a relationship not in the knowledge base is likely to be true. This paper combines insights from several previous link prediction models into a new embedding model STransE that represents each entity as a low-dimensional vector, and each relation by two matrices and a translation vector. STransE is a simple combination of the SE and TransE models, but it obtains better link prediction performance on two benchmark datasets than previous embedding models. Thus, STransE can serve as a new baseline for the more complex models in the link prediction task. ... : V1: In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016. V2: Corrected citation to (Krompa{\ss} et al., 2015). V3: A revised version of our NAACL-HLT 2016 paper with additional experimental results and latest related work ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1606.08140 https://dx.doi.org/10.48550/arxiv.1606.08140 BASE Hide details 18 STransE : a novel embedding model of entities and relationships in knowledge bases Nguyen, Dat Quoc; Sirts, Kairit; Qu, Lizhen. - : Red Hook, New York : Association for Computational Linguistics, 2016 BASE Show details

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