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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 陳信希 | |
dc.contributor.author | Yu-Hsiang Huang | en |
dc.contributor.author | 黃宇祥 | zh_TW |
dc.date.accessioned | 2021-06-17T02:40:25Z | - |
dc.date.available | 2018-08-18 | |
dc.date.copyright | 2018-08-18 | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017-08-16 | |
dc.identifier.citation | Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio, “Neural Machine Translation by Jointly Learning to Align and Translate”, Proceedings of International Conference on Learning Representations, 2015.
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/68887 | - |
dc.description.abstract | 自動反諷偵測,旨在使電腦理解人類反諷文字背後的真實意圖。過去的研究者 嘗試諸多人工抽取的複雜特徵,與各式經典機器學習方法。本研究探討如何通過詞 嵌入和深層神經網路將深度學習模型應用於此任務。本研究使用三種不同的深度 學習模型,分別為卷積神經網絡、循環神經網絡、和具注意力機制的循環神經網絡。 結果顯示具注意力機制的循環神經網絡在無脈絡的 Twitter 資料集和具脈絡的 Reddit 資料集達到最好的表現。此外,通過觀察由具注意力機制的循環神經網絡產 生的注意力權重向量,試圖窺見注意力機制如何幫助深度學習模型找出反諷語言 的文字線索。 | zh_TW |
dc.description.abstract | Automatic Irony Detection refers to making the computer understand the real intentions of the human behind the ironic language. Much work has been done using classic machine learning techniques together with various features. In contrast to sophisticated feature engineering, this research investigates how deep learning can be applied to the Automatic Irony Detection task with the help of word embedding. Three different deep learning models, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Attentive RNN, are explored. It shows that the Attentive RNN achieves the state-of-the-art performance on contextless and contextualized dataset. Furthermore, with a closer look at the attention vectors generated by Attentive RNN, an insight into how the attention mechanism helps find out the linguistic clues of ironic utterances is provided. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T02:40:25Z (GMT). No. of bitstreams: 1 ntu-106-R04922095-1.pdf: 2043756 bytes, checksum: dd9b8658988589920954203a674e2d0a (MD5) Previous issue date: 2017 | en |
dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iii Abstract iv Contents v List of Figures viii List of Tables ix Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Thesis Organization 2 Chapter 2 Related Work 3 2.1 Irony detection 3 2.1.1 Data Resource 3 2.1.2 Labeling 3 2.1.3 Contextless or Contextualized 4 2.1.4 Detection Approaches 4 2.2 Deep Learning 5 2.2.1 Convolutional Neural Network 5 2.2.2 Recurrent Neural Network 6 2.2.3 Attention Mechanism 7 Chapter 3 Methods 8 3.1 Framework 8 3.2 Encoding Methods 8 3.2.1 Convolutional Neural Network 8 3.2.2 Recurrent Neural Network 10 3.2.3 Attentive Recurrent Neural Network 11 3.2.4 Modification for Context Encoding 12 3.3 Project to Target Space 13 Chapter 4 Datasets 14 4.1 Twitter: A Contextless Dataset 14 4.1.1 Data Source 14 4.1.2 Dataset Collection 15 4.1.3 Basic Statistics of the Dataset 16 Figure 4‑2. Ratio of sarcastic/non-sarcastic instances in training data 16 4.2 Reddit: A contextualized dataset 17 4.2.1 Data Source 17 4.2.2 Dataset Collection 18 4.2.3 Basic Statistics of the Dataset 19 Chapter 5 Experiments 21 5.1 Contextless Experiment 21 5.1.1 Preprocessing 21 5.1.2 Baseline 21 5.1.3 Evaluation 22 5.1.4 Parameter Settings 22 5.2 Contextualized Experiment 23 5.2.1 Preprocessing 23 5.2.2 Baseline 23 5.2.3 Evaluation 24 5.2.4 Parameter Settings 24 Chapter 6 Discussion 26 6.1 Attention Weight Plots 26 6.1.1 Twitter 26 6.1.2 Reddit 27 6.2 The Issues of the Contextualization 29 6.3 The Correlation with the Metadata 32 6.3.1 Voted Score 32 6.3.2 Topic 33 6.4 Word Embedding Pre-training 34 6.5 Error Analysis 35 6.5.1 Inaccessible Context 35 6.5.2 Noisy Self-Annotated Label 36 Chapter 7 Conclusion 37 References 38 | |
dc.language.iso | en | |
dc.title | 基於具注意力機制類神經網路的反諷偵測 | zh_TW |
dc.title | Irony Detection Using Attentive Neural Network | en |
dc.type | Thesis | |
dc.date.schoolyear | 105-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 林川傑,禹良治,蔡宗翰 | |
dc.subject.keyword | 反諷偵測,循環類神經網路,注意力機制, | zh_TW |
dc.subject.keyword | Irony Detection,Recurrent Neural Network,Attention Mechanism, | en |
dc.relation.page | 38 | |
dc.identifier.doi | 10.6342/NTU201703535 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2017-08-17 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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