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Extracting biomedical entity relations from the literature using a recursive neural network

Extracting biomedical entity relations from the literature using a recursive neural network

자료유형
학위논문
개인저자
임상락 林床洛
서명 / 저자사항
Extracting biomedical entity relations from the literature using a recursive neural network / Sang Rak Lim
발행사항
Seoul :   Graduate School, Korea Unversity,   2019  
형태사항
vii, 69장 ; 26 cm
기타형태 저록
Extracting Biomedical Entity Relations from the Literature using a Recursive Neural Network   (DCOLL211009)000000083184  
학위논문주기
학위논문(박사)-- 고려대학교 대학원: 컴퓨터·전파통신공학과, 2019. 2
학과코드
0510   6YD36   356  
일반주기
지도교수: 강재우  
서지주기
참고문헌: 장 61-69
이용가능한 다른형태자료
PDF 파일로도 이용가능;   Requires PDF file reader(application/pdf)  
비통제주제어
relation extraction , recursive neural network,,
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245 1 0 ▼a Extracting biomedical entity relations from the literature using a recursive neural network / ▼d Sang Rak Lim
246 1 1 ▼a 문헌에서 recursive neural network를 활용하여 biomedical 객체 관계를 추출하는 방법에 관한 연구
260 ▼a Seoul : ▼b Graduate School, Korea Unversity, ▼c 2019
300 ▼a vii, 69장 ; ▼c 26 cm
500 ▼a 지도교수: 강재우
502 1 ▼a 학위논문(박사)-- ▼b 고려대학교 대학원: ▼c 컴퓨터·전파통신공학과, ▼d 2019. 2
504 ▼a 참고문헌: 장 61-69
530 ▼a PDF 파일로도 이용가능; ▼c Requires PDF file reader(application/pdf)
653 ▼a relation extraction ▼a recursive neural network
776 0 ▼t Extracting Biomedical Entity Relations from the Literature using a Recursive Neural Network ▼w (DCOLL211009)000000083184
900 1 0 ▼a 강재우 ▼g 姜在雨, ▼e 지도교수
945 ▼a KLPA

전자정보

No. 원문명 서비스
1
Extracting biomedical entity relations from the literature using a recursive neural network (26회 열람)
PDF 초록 목차
No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/학위논문서고/ 청구기호 0510 6YD36 356 등록번호 123060847 도서상태 대출가능 반납예정일 예약 서비스 B M
No. 2 소장처 과학도서관/학위논문서고/ 청구기호 0510 6YD36 356 등록번호 123060848 도서상태 대출가능 반납예정일 예약 서비스 B M
No. 3 소장처 세종학술정보원/5층 학위논문실/ 청구기호 0510 6YD36 356 등록번호 153081446 도서상태 대출가능 반납예정일 예약 서비스
No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/학위논문서고/ 청구기호 0510 6YD36 356 등록번호 123060847 도서상태 대출가능 반납예정일 예약 서비스 B M
No. 2 소장처 과학도서관/학위논문서고/ 청구기호 0510 6YD36 356 등록번호 123060848 도서상태 대출가능 반납예정일 예약 서비스 B M
No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 세종학술정보원/5층 학위논문실/ 청구기호 0510 6YD36 356 등록번호 153081446 도서상태 대출가능 반납예정일 예약 서비스

컨텐츠정보

초록

Due to the trendy word “Big Data”, it is widely known that a large volume of data has inherent values. Therefore, it is becoming essential to analyze data in many areas ranging from social media to scientific literature. Among the various fields, the bioinformatics is considered as an important emerging field as to technological advancement is crucial and the domain expert is limited in number. The bioinformatics is an interdisciplinary field that combines data mining and biomedical domain, and it has two purposes. One is to extract patterns from data to gain insight that is hard to detect. The other is to reduce the manual effort in laborious task of analyzing data.
Understanding the relationship of entities in the biomedical field is a vital issue. Especially, it is necessary to extract entity relationships to aim for precision medical care that provides customized healthcare services. The main approach of this thesis is based on the Recursive Neural Network approach. The Recursive Neural Network model receives inputs in a form of a parsed tree. The parsed tree is a grammatical structure of a given sentence, so that the model can learn the sentence structure understood by humans. This thesis deals with two studies that focus on the relationship between two important words in a biomedical paper. The first study extracts possible adverse effects in drug combinations, and the second study extracts relationships such as activator / inhibitor in the chemical-protein relationship.

목차

1 Introduction  1
2 Preliminaries: Recursive Neural Network  3
 2.1 Text Mining  3
  2.1.1 Recursive Neural Network and Parsed Tree  4
  2.1.2 An Overview of Recursive Neural Network Models  4
  2.1.3 A brief comparison between the Recurrent and the Recursive Neural 
Networks  7
 2.2 Future Directions  9
3 Drug drug interaction extraction from the literature using a recursive 
neural network 11
 3.1 Background and Problem Definition  11
 3.2 Materials and methods  13
  3.2.1 Model development  13
  3.2.2 Preprocessing  15
  3.2.3 Parsing sentences  17
  3.2.4 Word embedding  18
  3.2.5 Recursive neural network with treeLSTM  18
 3.3 Evaluation Results  23
  3.3.1 Experimental settings  23
  3.3.2 DDI13 data  24
  3.3.3 PK DDI data  28
 3.4 Discussion  32
  3.4.1 Robustness of our model  32
  3.4.2 Regularization analysis  32
4 Chemicalgene relation extraction challenge results using recursive neural 
network models 34
 4.1 Background and Problem Definition  34
 4.2 Materials and methods  36
  4.2.1 Pre-processing  38
  4.2.2 Parsing sentences  39
  4.2.3 Subtree containment feature generation  39
  4.2.4 Position feature generation  39
  4.2.5 Word Embedding  41
  4.2.6 Recursive neural network with tree-LSTM  42
  4.2.7 Ensemble method  44
  4.2.8 Implementation detail  44
 4.3 Post-challenge enhancements  45
  4.3.1 Additional pre-processing  45
  4.3.2 SPINN with additional pre-processing  45
 4.4 Evaluation Results  47
  4.4.1 Data corpus  47
  4.4.2 Hyperparameter  47
  4.4.3 Performance  49
  4.4.4 Summary and future directions  51
5 Discussion and Conclusion  53
 5.1 Discussion  53
  5.1.1 SPINN model for the Drug-Drug Interaction Extraction Task  53
  5.1.2 Error analysis from the Drug-Drug Interaction Extraction Task  55
  5.1.3 Error analysis from the Chemical-Protein Interaction Extraction Task  58
 5.2 Conclusion  59
Bibliography  61