博碩士論文 107423005 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:12 、訪客IP:3.238.98.214
姓名 黃軍儒(Chun-Ru Huang)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 人工合成文本之資料增益於不平衡文字分類問題
(Data Augmentation for Imbalanced Classification with Synthetic Text)
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2025-7-10以後開放)
摘要(中) 類別不平衡問題會因為各類別分布的高度不平均而產生。在現實生活中,不平衡文字分類任務時常發生,而文本分類器通常因為缺乏次要類別訓練數據而過度擬合於主要類別,導致在次要類別的分類表現不佳。
因此在本論文中,我們提出用各種不同的文字生成模型(MLE, SeqGAN, VAE, GPT-2)生成合成文本,並且資料增益在次要類別上。在我們的實驗中,我們將探討合成文本和真實資料在資料增益上的差距表現,以及比較合成文本與傳統的採樣方法、同義詞替換之方法之間的有效性,不同的文字表達法也將會被納入我們的觀察當中。
從我們的結果顯示,基於文字生成模型生成的合成文本用於資料增益可以解決類別不平衡的文字分類問題以及缺乏次要類別資料的問題。我們發現我們所提出的方法比先前的過採樣方法(如SMOTE)及同義詞替換方法的表現來的好。
再者,我們採用長文本及短文本這兩種角度觀察,發現不同的文字生成模型會依據其輸入的資料量大小及文本的長度,其增益的表現會有所不同。
摘要(英) Class imbalance exists when class distributions are heavily skewed. It is commonly found in many real-world text classification tasks. Text classifiers usually underperform on minor classes because of lack of training data, which is not desirable especially when minor classes are of interest.
We propose to apply different text generation models (MLE, SeqGAN, VAE, GPT-2) to generate synthetic text for data augmentation on minor classes. In our experiments, we evaluate the effectiveness of synthetic text against traditional sampling method, synonym replacement method and real-world text in terms of classification performance. Various text representations will also be discussed.
Our results show that synthetic text generated from text generation model for data augmentation can solve the problem of class imbalance and the problem of insufficient minority data. We found that the performance of our approach is better than previous oversampling method (SMOTE) and synonym replacement method. We also discover that different text generation models will perform different performances based on the dataset size and sentence length.
關鍵字(中) ★ 自然語言生成
★ 類別不平衡
★ 文字分類
★ 資料增益
關鍵字(英) ★ Natural Language Generation
★ class imbalance
★ text classification
★ data augmentation
論文目次 摘要 I
Abstract II
Acknowledgements III
Table of Contents IV
List of figures V
List of tables VI
1. Introduction 1
1.1. Research Overview 1
1.2. Research Motivation 2
1.3. Research Objective 2
1.4. Thesis Structure 3
2. Related Work 4
2.1. Class Imbalance in Text Classification 4
2.1.1. Oversampling 8
2.1.2. Under-sampling 8
2.1.3. Synthetic Minority Over-sampling Technique (SMOTE) 9
2.2. Data Augmentation 10
2.2.1. Synonym replacement 15
2.2.2. Back-translation 15
2.2.3. MixMatch 16
2.2.4. Data Augmentation for non-text data 16
2.3. Text generation 17
2.3.1. Maximum likelihood estimation 18
2.3.2. Sequence Generative Adversarial Nets 19
2.3.3. Variational Auto-Encoders 22
2.3.4. GPT-2 23
2.4. Word representation 24
2.4.1. TF-IDF 24
2.4.2. Word2vec 25
2.4.3. GloVe 27
2.5. Evaluation Metrics 28
2.5.1. G-mean 28
2.5.2. Minor F1 score, Major F1 score 29
3. Experiment 30
3.1. Overview 30
3.2. Datasets 31
3.2.1. Quora Insincere Question 31
3.2.2. Twitter Sentiment analysis 31
3.2.3. Amazon Fine Food Review 31
3.2.4. Toxic comment 32
3.2.5. Yelp sentiment analysis 32
3.2.6. SST-2 32
3.3. Experimental Settings 33
3.3.1. Preprocessing 33
3.3.2. Generative models and Classifier model settings 34
3.4. Experiment Design 36
3.4.1. Experiment Ⅰ: Text generation for data augmentation 36
3.4.2. Compare with other data augmentation methods (SMOTE, EDA) and real text 37
3.4.3. Text classification 38
3.4.4. Experiment Ⅱ: Balanced an imbalance dataset 39
4. Results 40
4.1. ExperimentⅠ 40
4.1.1. Short text dataset 40
4.1.2. Long text dataset 46
4.1.3. Answer our Questions 50
4.2. Experiment Ⅱ 52
4.2.1. Short text dataset 52
4.2.2. Long text dataset 60
4.2.3. Answer Questions 66
5. Conclusion 68
5.1. Summary 68
5.2. Contribution 68
5.3. Limitations 68
5.4. Future work 69
6. Reference 71
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指導教授 柯士文(Shih-Wen Ke) 審核日期 2020-7-16
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