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姓名	陳筱萱(Hsiao-Hsuan Chen)  查詢紙本館藏	畢業系所	資訊工程學系
論文名稱	(Evaluation of Stacked Embeddings for Sarcasm Detection Using Auxiliary Sentence)
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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]   [檢視]  [下載] 本電子論文使用權限為同意立即開放。 已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。 請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。
摘要(中)	隨著社交媒體的發展，網路上充斥著大量的訊息。為了能夠快速地分析這些大量訊息中的正面及負面情緒，情緒分析已成為自然語言處理中重要的議題。在不同類型的情感分析中，諷刺偵測尤其扮演著極重要的角色。因為當一個句子含有諷刺意味時，其表面含義將會與其核心表達意思相反。為了避免錯誤的判斷，本研究提出了兩種提高諷刺偵測準確度的技術：構建輔助句與堆疊多個嵌入。在構建輔助句的部分，我們提出了兩種方法: AUX-Q和AUX-POSNEG。而堆疊多個嵌入的部分，我們將Transformer-based Embeddings和Static Embeddings堆疊合併成一個新的嵌入。我們在兩個諷刺偵測資料集上實驗我們提出的技術。結果表明，我們提出的方法分別在SemEval 2018 Task 3資料集與News Headlines資料集上，將目前最先進方法的錯誤率降低了7.23%和33.68%。
摘要(英)	With the development of social media, the Internet is full of messages. In or der to quickly analyze the positive and negative emotions in a large number of messages, sentiment analysis has become an important issue in natural language processing. Among different types of sentiment analyses, sarcasm detection plays an important role. When a sentence is sarcastic, its meaning will be opposite to that of the core expression. In order to avoid wrong judgments, this research proposes two techniques to improve the accuracy of sarcasm detection: Auxiliary Sentence Construction and Stacking Multiple Embeddings. For the construction of auxiliary sentences, two methods are proposed: AUX-Q and AUX-POSNEG. For stacking multiple embeddings, Transformer-based Embeddings and Static Embeddings are combined into a new embedding. We experimented our proposed techniques on two sarcasm detection datasets. The results showed that our proposed methods reduce the error rate of the state-of-the-art sarcasm detection by 7.23% on SemEval 2018 Task 3 dataset and 33.68% on News Headlines dataset.
關鍵字(中)	★ 諷刺偵測 ★ 詞嵌入堆疊 ★ 輔助句子	關鍵字(英)	★ Sarcasm Detection ★ Embedding Stacking ★ Auxiliary Sentence
論文目次	1 Introduction p.1 2 RelatedWork p.3 2.1 Approaches based on unexpectedness and contradictory factors p.3 2.2 Content-based approaches p.4 2.3 Deep Learning approaches p.5 2.4 Two-Stage approaches p.5 3 Preliminary p.7 3.1 Static Embedding p.7 3.1.1 Word2Vec p.7 3.1.2 Global Vectors for Word Representation (GloVe) p.8 3.1.3 FastText p.9 3.2 Dynamic Embedding p.9 3.2.1 Embeddings from Language Models (ELMo) p.9 3.2.2 Bidirectional Encoder Representations from Transformers (BERT) p.10 3.2.3 A LITE BERT (ALBERT) p.12 3.2.4 XLNet p.12 3.2.5 Robustly Optimized BERT Approach (RoBERTa) p.14 3.2.6 FLAIR Embedding p.15 4 Design p.17 4.1 Dataset and Data Pre-processing p.17 4.2 Proposed Method p.18 4.2.1 Constructing Auxiliary Sentence p.18 4.2.2 Stacking Multiple Embeddings p.21 5 Performance p.24 5.1 Segmentation of Datasets and Experimental Setup p.24 5.2 Evaluation Metric p.25 5.3 Experimental Results p.27 5.3.1 SemEval 2018 Task 3 p.27 5.3.2 News Headlines Dataset For Sarcasm Detection p.30 5.3.3 Comparison between different models p.33 6 Conclusions p.34 Reference p.35
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指導教授	孫敏德(Min-Te Sun)	審核日期	2020-7-29
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