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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳信希(Hsin-Hsi Chen) | |
| dc.contributor.author | Chin-Ho Lin | en |
| dc.contributor.author | 林勤和 | zh_TW |
| dc.date.accessioned | 2021-06-17T01:18:28Z | - |
| dc.date.available | 2022-08-20 | |
| dc.date.copyright | 2017-08-20 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-08-11 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/67051 | - |
| dc.description.abstract | 透過資訊擷取系統以自動化方式建立知識庫，仍面臨許多挑戰。其中，對應知識在自然語言與知識庫中的字詞表示形式差異仍是一項問題。本篇論文以機器自動學習的方法，將知識三元組中的關係片語從自然語言形式的關係樣式，轉換為知識庫格式的關係謂語。我們在向量空間上訓練一個字詞表示模型，並藉此建立連結語意相等的關係樣式與關係謂語。相較於前人們的研究，我們所建立的關係片語對應表，不僅準確而且達到可觀的高覆蓋率。 | zh_TW |
| dc.description.abstract | Directly adding the knowledge triples obtained from open information extraction systems into a knowledge base is often impractical due to a vocabulary gap between natural language expressions and knowledge base representation. This thesis aims at learning to map relational phrases in triples from natural-language-like statement to knowledge base predicate format. We train a word representation model on a vector space and link each natural language relational pattern to semantically equivalent knowledge base predicate. Our mapping result shows not only high quality, but also promising coverage on relational phrases compared to previous researches. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T01:18:28Z (GMT). No. of bitstreams: 1 ntu-106-R04922103-1.pdf: 12777554 bytes, checksum: 345a0112a4265a579018495e0dfcf529 (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 摘要 iii ABSTRACT iv CONTENTS v LIST OF FIGURES vii LIST OF TABLES viii Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Organization 2 Chapter 2 Related Work 3 2.1 Embedding Model 3 2.1.1 Word Embedding Model 3 2.1.2 Knowledge Base Embedding Model 4 2.2 Knowledge Base Construction 6 2.2.1 Open Information Extraction System 6 2.2.2 Relation Extraction 6 Chapter 3 Linguistic Resources 9 3.1 Terminology 9 3.2 English Datasets 10 3.3 Chinese Datasets 11 Chapter 4 Relational Mapping 13 4.1 EB: Entity Bridging with Alias Resolution 14 4.2 DR: Decompose Relational Phrases and Introduce Additional NL Text 17 4.3 TF: Filter Relational Mapping by Argument Type Constraint 20 Chapter 5 Experiments 23 5.1 Dataset and Experiment Setting 23 5.2 Triple Linking Task 26 5.3 Human Verification Task 30 5.4 Error-Case Discussion 34 Chapter 6 Conclusion and Future Work 35 References 36 | |
| dc.language.iso | en | |
| dc.subject | 關係片語 | zh_TW |
| dc.subject | 知識庫建立 | zh_TW |
| dc.subject | 字詞表示形式 | zh_TW |
| dc.subject | 關係片語映射 | zh_TW |
| dc.subject | Knowledge Base Construction | en |
| dc.subject | Relational Phrases | en |
| dc.subject | Relational Mapping | en |
| dc.subject | Word Representation | en |
| dc.title | 學習將自然語言敘述映射為知識圖譜表示形式以利知識庫之建立 | zh_TW |
| dc.title | Learning to Map Natural Language Statements into Knowledge Base Representations for Knowledge Base Construction | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 李政德(Cheng-Te Li),古倫維(Lun-Wei Ku),張嘉惠(Chia-Hui Chang) | |
| dc.subject.keyword | 知識庫建立,關係片語,關係片語映射,字詞表示形式, | zh_TW |
| dc.subject.keyword | Knowledge Base Construction,Relational Phrases,Relational Mapping,Word Representation, | en |
| dc.relation.page | 38 | |
| dc.identifier.doi | 10.6342/NTU201703086 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2017-08-14 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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| ntu-106-1.pdf 未授權公開取用 | 12.48 MB | Adobe PDF |
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