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系統識別號 U0026-3108202014111400
論文名稱(中文) 基於雙向觀點及主題資訊之立場偵測
論文名稱(英文) Bidirectional Perspective with Topic Information for Stance Detection
校院名稱 成功大學
系所名稱(中) 資訊工程學系
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 108
學期 2
出版年 109
研究生(中文) 林聖軒
研究生(英文) Sheng-Xuan Lin
學號 P76061425
學位類別 碩士
語文別 英文
論文頁數 47頁
口試委員 指導教授-高宏宇
口試委員-張俊盛
口試委員-黃仁暐
口試委員-張嘉惠
口試委員-莊坤達
中文關鍵字 立場偵測  雙向觀點  預訓練語言模型  主題模型 
英文關鍵字 Stance detection  bidirectional perspective  pre-trained model  topic model 
學科別分類
中文摘要 由於網路的便利,有許多社群網站或網路新聞會大量傳播錯誤的假新聞,這些假新聞會引起社會的恐慌和不安,或達成政治上的目的。自動假新聞偵測可以快速的對假新聞進行分類,並在事件發生時迅速幫助社會澄清訊息的真假,而無需進行長時間繁雜的人工檢查。通過分析群眾或新聞立場來幫助判斷新聞的真實性是目前主流的方法之一。因此,立場偵測已成為近年來受到重視的研究領域,如何準確的檢測立場已成為檢測假新聞的首要目標。
這項研究的目標是準確地偵測新聞的立場,以指導對假新聞的識別。本文提出了一種基於預訓練的BERT語言模型的立場偵測網路,BERT在外部語料庫上使用無監督的學習訓練,從而獲得通用的語言知識。近年來,基於BERT的遷移學習方法已被廣泛使用,並取得了優異的成績。下游任務可以受益於預訓練模型中所學到的先驗語言知識。先前的大多數的方法在對立場進行分類時僅使用了單一方向的推理資訊,這可能會遺漏一些重要信息。因此,我們提出了一種雙向推理立場偵測模型,該模型可以利用雙向觀點的訊息來總結出更加全面的資訊來幫助立場分類。最後,我們將立場偵測任務定義為階層結構的任務,並使用階層分類系統以及加入文本的主題資訊來幫助立場的分類。實驗結果表明,我們的模型能夠更加準確地分類立場。
英文摘要 Because of the convenience of the Internet, there are many websites or online news spread misinformation, cause panic and trepidation in society. Automatic fake news detection can classify fake news and help the society to clarify the information is true or false without human checking. Detecting fake news by analyzing the stance is one of the mainstream methods, stance detection has become a new popular research field in recent years. How to accurately detect stance has become the primary goal of detecting fake news.
This research aims to detect the news stance accurately, and we propose a method based on a pre-trained BERT language model. Most of the previous work only used the knowledge of single inference direction when classifying the stance, which may lose some important information. Therefore, we propose a bidirectional inference stance detection model, which can leverage bidirectional perspective information to classify the stance more comprehensively. We also define the stance detection task as a hierarchical structure task, and use the hierarchical classification and incorporate the topic information to help the stance classification. Experiment results show that our model can classify the stance more accurately.
論文目次 中文摘要 I
Abstract II
TABLE LISTING VI
FIGURE LISTING VII
1 INTRODUCTION 1
1.1 Background 1
1.2 Motivation 3
1.3 Our work 7
1.4 Paper structure 7
2 Related Work 9
2.1 Stance detection 9
2.2 Bidirectional Encoder Representations from Transformers (BERT) 14
2.3 LDA Topic model 18
2.4 Variational Autoencoder Topic Models 19
3 Method 21
3.1 Data Preprocessing 22
3.2 Features Extraction 22
3.3 Bidirectional perspective 23
3.4 Word level attention 25
3.5 Prediction Layer 27
3.6 Hierarchical classification 28
4 Experiment 30
4.1 Dataset 30
4.2 Model Parameter 32
4.3 Evaluation Matrices 32
4.4 Result 33
4.5 Perspective Analysis 35
4.6 Complementarity of different inference direction 37
4.7 Topic word analysis 41
4.8 Ablation Study 42
5 Conclusion 44
6 References 45
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