Advanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa
➤ Gửi thông báo lỗi ⚠️ Báo cáo tài liệu vi phạmNội dung chi tiết: Advanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa
Advanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa
V IETNAM NATIONAL UNIV ERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYCAN DI Y CATADVANCED DEEP LEARNING MODELS AND APPLICATIONS IN SEMANTIC RE Advanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa ELATION EXTRACTIONMASTER THESISMajor: Computer ScienceHANOI-2019VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYCan Duy CatADVANCED DEEP LEARNING MODELS AND APPLICATIONS LN SEMANTIC RELATION EXTRACTIONMASTER THESISMajor: Computer ScienceSupervisor: Assoc.Prof. Ila Quang 1 Advanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa huyAssoc.Prof. Chng Eng SiongHA NOI - 2019AbstractRelation Extraction (RE) is one of the most fundamental task of Natural Language Processing (NLP) anAdvanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa
d Information Extraction (IE). To extract the relationship between two entities in a sentence, two common approaches are (1) using their shortest depeV IETNAM NATIONAL UNIV ERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYCAN DI Y CATADVANCED DEEP LEARNING MODELS AND APPLICATIONS IN SEMANTIC RE Advanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa vantage of either missing or redundant information. In this work, we propose a novel model that combines the advantages of these two approaches. This is based on the basic information in the SDP enhanced with information selected by several attention mechanisms with kernel Idlers, namely Rb.SP (Rich Advanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa er-but-Smarter SDP). Io exploit the representation behind the RbSP structure effectively, we develop a combined Deep Neural Network (DNN) with a LongAdvanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa
Short-Term Memory (LSTM) network on word sequences and a Convolutional Neural Network (CNN) on RbSP.Furthermore, experiments on the task of RE proved V IETNAM NATIONAL UNIV ERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYCAN DI Y CATADVANCED DEEP LEARNING MODELS AND APPLICATIONS IN SEMANTIC RE Advanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa nal embedding that combines several dominant linguistic as well as architectural features and (ii) dependency tree normalization techniques for generating rich representations for both words anil dependency relations in the SDP.Experimental results on both general data (SemEval-2010 Task 8) and biom Advanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa edical data (BioCreative V Track 3 CDR) demonstrate the out-performance of our proposed model over all compared models.Keywords: Relation Extraction.Advanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa
Shortest Dependency Path. Convolutional Neural Network, Long Short-Term Memory. Attention Mechanism.iiiAcknowledgements1 would first like to thank my V IETNAM NATIONAL UNIV ERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYCAN DI Y CATADVANCED DEEP LEARNING MODELS AND APPLICATIONS IN SEMANTIC RE Advanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa nsistently allowed this paper to be my own work, but steered me in the right the direction whenever he thought I needed it.1 also want to acknowledge my co-supervisor Assoc.Prof Chng Eng Siong from Nanyang Technological University, Singapore for offering me the internship opportunities at NTU, Singa Advanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa pore and leading me working on diverse exciting projects.Furthermore. I am very grateful to my external advisor MSc. Le Hoang Quynh. for insightful coAdvanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa
mments both in my work and in this thesis, for her support, and for many motivating discussions.In addition. 1 have been very privileged to get to knoV IETNAM NATIONAL UNIV ERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYCAN DI Y CATADVANCED DEEP LEARNING MODELS AND APPLICATIONS IN SEMANTIC RE Advanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa on, and for all the fun we have had over the last two years. 1 thank to MSc. Ho Thi Nga and MSc. Vu Thi Ly for continuous support during the time in Singapore.Finally. 1 must express my very profound gratitude to my family for providing me w ith unfailing support and continuous encouragement through Advanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa out my years of study and through the process of researching and writing this thesis. This accomplishment would not have been possible without them.ivAdvanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa
DeclarationI declare that the thesis has been comjx>scd by myself and that the work has not Ik* submitted for any other degree or professional qualifiV IETNAM NATIONAL UNIV ERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYCAN DI Y CATADVANCED DEEP LEARNING MODELS AND APPLICATIONS IN SEMANTIC RE Advanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa ntribution and those of the other authors to this work have been explicitly indicated below. I confirm that appropriate credit has been given within this thesis where reference has been made to the work of others.The model presented in Chapter 3 and the results presented in Chapter 4 was previously Advanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa published in the Proceedings of AC1IDS 2019 as “Improving Semantic Relation Extraction System with Compositional Dependency Unit on Enriched ShortestAdvanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa
Dependency Path" and NAACL-HTL 2019 as "A Richer-but-Smarter Shortest Dependency Path with Attentive Augmentation for Relation Extraction" by myself eV IETNAM NATIONAL UNIV ERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYCAN DI Y CATADVANCED DEEP LEARNING MODELS AND APPLICATIONS IN SEMANTIC RE Advanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa to the best of my knowledge, my thesis does not infringe upon anyone’s copyright nor violate any proprietary rights and that any ideas, techniques, quotations. or any other material from the work of other people included in my thesis, published or otherwise, tire fully acknowledged in accordance wit Advanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa h the standard referencing practices. Furthermore, to the extent that 1 have included copyrighted material. 1 certify that 1 have obtained a written pAdvanced deep learning models and applications in semantic relation extraction các mô hình học sâu tiên tiến và ứng dụng trong trích chọn quan hệ ngữ nghĩa
ermission from the copyright owner(s) to include such material(s) in my thesis and have fully authorship to improve these materials.Master studentCan V IETNAM NATIONAL UNIV ERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYCAN DI Y CATADVANCED DEEP LEARNING MODELS AND APPLICATIONS IN SEMANTIC REV IETNAM NATIONAL UNIV ERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGYCAN DI Y CATADVANCED DEEP LEARNING MODELS AND APPLICATIONS IN SEMANTIC REGọi ngay
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