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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳信希(Hsin-Hsi Chen) | |
| dc.contributor.author | Yang-Yin Lee | en |
| dc.contributor.author | 李昂穎 | zh_TW |
| dc.date.accessioned | 2021-06-17T06:01:55Z | - |
| dc.date.available | 2021-02-12 | |
| dc.date.copyright | 2019-02-12 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-01-31 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71499 | - |
| dc.description.abstract | 隨著自然語言處理工作的需求增加，近年來對於較好的詞分散表示法（詞向量）及詞義分散表示法（詞義向量）的需求在增加當中。在本篇研究當中，我們先探討在詞向量中的不正常維度，然後提出結合詞向量與本體論之模型。結合的方法分為三個部分來討論：直接結合方法，支持向量迴歸方法及利用後適配方法。在詞義向量方面，我們首先提出了能夠利用文本即本體論資訊學習更好詞義向量的聯合詞義後適配模型，並且一般化提出來的模型。 | zh_TW |
| dc.description.abstract | With the increasing number of natural language processing tasks, the need for better representation of words (word embedding) and senses (sense embedding) is getting higher in recent years. In this study, we firstly discuss the problem of abnormal dimensions in word embeddings, and then propose models that combine word embedding with ontology. The combination is discussed in three ways: directly combination approach, support vector regression approach and retrofitting approach. In sense embedding, we firstly propose a joint sense retrofitting model that learns better sense embedding from contextual and ontological information, and then generalize the proposed model. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T06:01:55Z (GMT). No. of bitstreams: 1 ntu-108-D02922015-1.pdf: 2834796 bytes, checksum: 40494c21d90b8d84121b68e2cf093b0a (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES ix LIST OF TABLES xi Chapter 1 Introduction 1 1.1 Research Motivation and Objectives 1 1.2 Organization of this Dissertation 8 Chapter 2 Related Works 10 2.1 Linguistic Resource Based Approaches 11 2.2 Corpus Based Approaches 11 2.3 Hybrid Approaches 13 2.3.1 In-processing 13 2.3.2 Post-processing 13 Chapter 3 Resources 15 3.1 WordNet 15 3.2 The Chinese Synonym Ontology Tongyici Cilin 16 3.3 The Paraphrase Database 17 3.4 Semantic Relatedness Datasets 17 3.4.1 RG65 17 3.4.2 WordSim-353 (WS353) 18 3.4.3 Chinese WordSim (CWS) 18 3.4.4 YP130 18 3.4.5 MEN 18 3.4.6 SimLex-999 (SL999) 19 3.4.7 Rare Words (RW) 19 3.4.8 MTurk 20 3.4.9 Stanford's Contextual Word Similarities (SCWS) Dataset 20 3.4.10 Summary of the Datasets 20 3.5 Semantic Difference Dataset 25 3.6 Synonym Selection Datasets 25 3.6.1 ESL-50 25 3.6.2 RD-300 25 3.6.3 TOEFL-80 25 Chapter 4 The Abnormal Dimensions in Word Embedding 27 4.1 The Problem 27 4.2 Transforming Abnormal Dimensions 30 4.2.1 Dimension Removal 30 4.2.2 Offset Transformation 31 4.2.3 Uniform Transformation 31 4.2.4 Harmonic Series Transformation 32 4.3 Experiments 33 4.3.1 Measures 33 4.3.2 Experimental Setup 34 4.3.3 Experimental Results 34 4.4 Other Word Embedding with Abnormal Dimensions 37 4.5 The Impact of Dimension Removal and Retrofitting 38 4.6 Abnormal Dimensions in Chinese Word Embedding 40 4.7 Summary 42 Chapter 5 Combination of Word Embedding and WordNet 44 5.1 The Model 44 5.2 Experiment 44 5.3 Summary 47 Chapter 6 Support Vector Regression Approach 48 6.1 The Model 48 6.2 Experiment 49 6.2.1 Analysis of the WordNet Features 50 6.3 Summary 52 Chapter 7 Structural-fitting of Word Embedding 53 7.1 Structural-fitting of Word Vectors 55 7.1.1 Fine2coarse Approach 55 7.1.2 Coarse2fine Approach 56 7.2 Experiment 57 7.2.1 Experimental Results 59 7.3 Summary 62 Chapter 8 Joint Sense Retrofitting from Contextual and Ontological Information 63 8.1 Introduction 63 8.2 Joint Sense Retrofitting 64 8.3 Datasets and Experimental Setup 65 8.4 Results and Discussions 68 8.5 Summary 69 Chapter 9 GenSense: A Generalized Sense Retrofitting Model 71 9.1 Introduction 71 9.2 Generalized Sense Retrofitting Model 73 9.2.1 Standardization on the Dimensions 77 9.2.2 Neighbor Expansion from the Nearest Neighbors 80 9.2.3 Combination of Standardization and Neighbor Expansion 82 9.3 Procrustes Analysis 82 9.3.1 Infer from Procrustes Analysis 84 9.4 Experiments 85 9.4.1 Experimental Setup 85 9.4.2 Semantic Relatedness 86 9.4.3 Contextual Word Similarity 86 9.4.4 Semantic Difference 87 9.4.5 Synonym Selection 87 9.5 Results and Discussion 88 9.5.1 Semantic Relatedness 88 9.5.2 Contextual Word Similarity 98 9.5.3 Semantic Difference 99 9.5.4 Synonym Selection 99 9.6 Summary 100 Chapter 10 Conclusion 102 10.1 Word Level 102 10.2 Sense Level 103 10.3 Future Works 104 REFERENCE 106 | |
| dc.language.iso | en | |
| dc.subject | 詞向量 | zh_TW |
| dc.subject | 本體論 | zh_TW |
| dc.subject | 語意關聯度 | zh_TW |
| dc.subject | 詞義向量 | zh_TW |
| dc.subject | word embedding | en |
| dc.subject | sense embedding | en |
| dc.subject | ontology | en |
| dc.subject | semantic relatedness | en |
| dc.title | 利用本體論及後適配技術於產生較佳之詞及詞義分散表示法 | zh_TW |
| dc.title | On Utilization of Ontology and Retrofitting Techniques for Better Distributed Representations of Words and Senses | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-1 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 鄭卜壬(Pu-Jen Cheng),李宏毅(Hung-yi Lee),吳宗憲(Chung-Hsien Wu),蔡宗翰(Tzong-Han Tsai),陳柏琳(Berlin Chen) | |
| dc.subject.keyword | 詞向量,詞義向量,本體論,語意關聯度, | zh_TW |
| dc.subject.keyword | word embedding,sense embedding,ontology,semantic relatedness, | en |
| dc.relation.page | 116 | |
| dc.identifier.doi | 10.6342/NTU201900341 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-01-31 | |
| 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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