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Incorporating neural models into open directory project based large-scale text classification

Incorporating neural models into open directory project based large-scale text classification

자료유형
학위논문
개인저자
이지민 李知玟
서명 / 저자사항
Incorporating neural models into open directory project based large-scale text classification / Ji-min Lee
발행사항
Seoul :   Graduate School, Korea Unversity,   2019  
형태사항
iv, 39장 : 도표 ; 26 cm
기타형태 저록
Incorporating Neural Models into Open Directory Project based Large-scale Text Classification   (DCOLL211009)000000083457  
학위논문주기
학위논문(석사)-- 고려대학교 대학원: 컴퓨터·전파통신공학과, 2019. 2
학과코드
0510   6D36   1093  
일반주기
지도교수: 이상근  
서지주기
참고문헌: 장 36-39
이용가능한 다른형태자료
PDF 파일로도 이용가능;   Requires PDF file reader(application/pdf)  
비통제주제어
Text classification , Neural network , Word embeddings,,
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005 20190416162523
007 ta
008 181226s2019 ulkd bmAC 000c eng
040 ▼a 211009 ▼c 211009 ▼d 211009
085 0 ▼a 0510 ▼2 KDCP
090 ▼a 0510 ▼b 6D36 ▼c 1093
100 1 ▼a 이지민 ▼g 李知玟
245 1 0 ▼a Incorporating neural models into open directory project based large-scale text classification / ▼d Ji-min Lee
260 ▼a Seoul : ▼b Graduate School, Korea Unversity, ▼c 2019
300 ▼a iv, 39장 : ▼b 도표 ; ▼c 26 cm
500 ▼a 지도교수: 이상근
502 0 ▼a 학위논문(석사)-- ▼b 고려대학교 대학원: ▼c 컴퓨터·전파통신공학과, ▼d 2019. 2
504 ▼a 참고문헌: 장 36-39
530 ▼a PDF 파일로도 이용가능; ▼c Requires PDF file reader(application/pdf)
653 ▼a Text classification ▼a Neural network ▼a Word embeddings
776 0 ▼t Incorporating Neural Models into Open Directory Project based Large-scale Text Classification ▼w (DCOLL211009)000000083457
900 1 0 ▼a Lee, Ji-min, ▼e
900 1 0 ▼a 이상근 ▼g 李尙根, ▼e 지도교수
945 ▼a KLPA

전자정보

No. 원문명 서비스
1
Incorporating neural models into open directory project based large-scale text classification (32회 열람)
PDF 초록 목차

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/학위논문서고/ 청구기호 0510 6D36 1093 등록번호 123060849 도서상태 대출가능 반납예정일 예약 서비스 B M
No. 2 소장처 과학도서관/학위논문서고/ 청구기호 0510 6D36 1093 등록번호 123060850 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

초록

In natural language processing, large-scale text classification has been utilized to capture the various topics in arbitrary texts. For large-scale text classification, many approaches have used the explicit representation model based on knowledge bases. Although such approaches exhibit promising results, their performance is limited to the associated knowledge base. In this thesis, we incorporate neural models into the large-scale text classification. To this end, we propose an attentive joint model and adaptive joint models that represent documents and categories using word embeddings adapted to the knowledge base. To demonstrate the efficacy of our strategy, we apply the proposed methodologies to the Open Directory Project (ODP)-based text classification task. The proposed methods outperform the recent state-of-the-art method in terms of micro-averaging F1-score, macro-averaging F1-score, and precision at k.

목차

Abstract 
Contents i 
List of Figures iii 
List of Tables iv 
1 Introduction 1
2 Preliminary 4
 2.1 ODP-based Text Classification 4
 2.2 Word Embeddings 5
 2.3 ODP-based Text Classification with Word Embeddings 6
3 Attentive Joint Model for ODP-based Text Classification 8
 3.1 Attentive Joint Model 8
 3.2 Semantic Similarity Method 12
4 Adaptive Joint Models for ODP-based Text Classification 14
 4.1 Adaptive Joint Model Trained by ODP Documents 14
 4.2 Adaptive Joint Model Trained by ODP Categories 16
 4.3 Classification in Adaptive Joint Models 17
5 Performance Evaluation 18
 5.1 Datasets 18
  5.1.1 ODP Dataset 18
  5.1.2 NYT Dataset 19
 5.2 Evaluation Metrics 19
  5.2.1 ODP Dataset 19
  5.2.2 NYT Dataset 19
 5.3 Experimental Setup 20
 5.4 Experimental Results 22
  5.4.1 Results of the ODP Dataset 22
  5.4.2 Results of the NYT Dataset 24
 5.5 Analysis of NYT Classification Results 26
6 Related Work 32
 6.1 Large-scale Text Classification 32
 6.2 ODP-based Text Classification 33
7 Conclusion 35
Bibliography 36
Acknowledgement 40

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