Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 이제민 | - |
| dc.contributor.author | Myeonghwa Lee | - |
| dc.date.accessioned | 2022-07-07T02:29:08Z | - |
| dc.date.available | 2022-07-07T02:29:08Z | - |
| dc.date.issued | 2021 | - |
| dc.identifier.uri | http://dgist.dcollection.net/common/orgView/200000363154 | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.11750/16675 | - |
| dc.description.statementofresponsibility | N | - |
| dc.description.tableofcontents | 1. Introduction 1 1.1 Motivation 2 1.2 Contributions 3 2. Background 5 2.1 Language Modeling 5 2.2 Context-independent and Context-sensitive Text Representation 6 2.3 Pre-train and Fine-tune Paradigm in the Field of Natural Language Processing 8 2.4 Semantic Role Labeling 9 2.5 Citation Intent Classification 10 3. Related Work 11 3.1 BERT (Bidirectional Transformers for Language Understanding) 11 3.2 BERT Variant Models 13 3.2.1 BERT Models Pre-trained with Domain-specific Corpus 13 3.2.2 BERT Models Pre-trained with New Tasks 14 3.2.3 BERT Models Pre-trained with Additional Features 15 4. Our Methods for OLAP-BERT 17 4.1 Method 1: Additional Features Affect Text Tokens Differently 17 4.2 Method 2: Additional Features Affect Text Tokens Equally 20 5. Our Datasets for OLAP-BERT 22 5.1 DBLP-RC: A Record-based Corpus 22 5.2 Record-based Labeled Datasets 24 5.2.1 DBLP-RDfSRL: A Record-based Dataset for Semantic Role Labeling 24 5.2.2 DBLP-RDfCIC: A Record-based Dataset for Citation Intent Classification 25 6. Experiments 26 6.1 Datasets 26 6.1.1 Record-based Corpus for Pre-training 26 6.1.2 Record-based Datasets for Fine-training 28 6.2 Experimental Setup 28 6.2.1 Pre-training for Natural Language Understanding Models 29 6.2.2 Fine-tuning for Task-specific Models 31 6.3 Experimental Results 32 6.3.1 Results of the Pre-training 32 6.3.2 Results of the Fine-tuning 34 7. Discussion 36 8. Conclusions 38 9. References 40 |
- |
| dc.format.extent | 44 | - |
| dc.language | eng | - |
| dc.publisher | DGIST | - |
| dc.subject | BERT, OLAP, NLU, NLP, SRL, BERT, OLAP, 자연어 이해, 자연어 처리, 의미역 결정 | - |
| dc.title | Improving the Performance of Natural Language Deep Learning Models by Using Dimension Attribute Values | - |
| dc.title.alternative | Dimension 속성값을 이용한 자연언어 딥러닝 모델의 성능 향상 | - |
| dc.type | Thesis | - |
| dc.identifier.doi | 10.22677/thesis.200000363154 | - |
| dc.description.degree | Master | - |
| dc.contributor.department | Information and Communication Engineering | - |
| dc.contributor.coadvisor | Min-Soo Kim | - |
| dc.date.awarded | 2021/02 | - |
| dc.publisher.location | Daegu | - |
| dc.description.database | dCollection | - |
| dc.citation | XT.IM 이34 202102 | - |
| dc.contributor.alternativeDepartment | 정보통신융합전공 | - |
| dc.embargo.liftdate | 2023-02-28 | - |
| dc.contributor.affiliatedAuthor | Myeonghwa Lee | - |
| dc.contributor.affiliatedAuthor | Jemin Lee | - |
| dc.contributor.affiliatedAuthor | Min-Soo Kim | - |
| dc.contributor.alternativeName | 이명화 | - |
| dc.contributor.alternativeName | Jemin Lee | - |
| dc.contributor.alternativeName | 김민수 | - |
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