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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 鄭卜壬 | |
dc.contributor.author | Pu-Chen Ni | en |
dc.contributor.author | 倪溥辰 | zh_TW |
dc.date.accessioned | 2021-06-17T07:14:51Z | - |
dc.date.available | 2022-07-19 | |
dc.date.copyright | 2019-07-19 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-07-16 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73037 | - |
dc.description.abstract | 具爭議性議題的新聞一直是閱聽人關注與討論的焦點,例如:美國牛肉開放進口、死刑廢除、多元成家等。不論是政治、經濟、教育、兩性、能源、環保等公共議題,新聞媒體常需報導不同的立場。若能從大量的新聞文件裡,快速搜尋各種爭議性議題中具特定立場的新聞,不但有助於人們理解不同立場對這些議題的認知與價值觀,對制定決策的過程而言,也相當有參考價值。
在本篇論文中,針對新聞立場檢索這個題目做了「一條龍式」的研究,包含創建資料集、標注規則定義、比較各種可能的檢索方法。並提出一種新穎的關聯回饋機制,比起傳統的關聯回饋更具效率,而實驗證明該機制亦能提高新聞立場檢索的表現。 | zh_TW |
dc.description.abstract | News with controversial topic has always been discussed by the audience. For example, US beef open for import, abolishing death penalty, and same-sex marriage, etc. Whether it is political, economic, educational, gender, energy, environmental protection and other public topics, the news media often need to report in different positions. If we can quickly search for news with specific positions from a large number of news documents, it will not only help people understand different positions, but also good for making decisions.
In this paper, we have done a study on the theme of news stance retrieval from beginning to end, including creating datasets, defining labeling rule, and comparing various possible methods. And a novel relevance feedback mechanism is proposed, which is more efficient than traditional relevance feedback, and the experiments show that the proposed mechanism can improve the performance of news stance retrieval. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T07:14:51Z (GMT). No. of bitstreams: 1 ntu-108-R06944032-1.pdf: 1082615 bytes, checksum: 9a17c24e8693dfe29feb3fbe7081ca65 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 誌謝 .............................................................................................i
中文摘要 ..................................................................................... ii ABSTRACT ................................................................................ iii CONTENTS ................................................................................iv LIST OF FIGURES........................................................................vi LIST OF TABLES ........................................................................ vii 1 Introduction...............................................................................1 2 Related Works ..........................................................................4 3 Methodology ............................................................................6 3.1 Information Retrieval Part ......................................................6 3.2 A Novel Relevance Feedback Mechanism .............................7 3.3 Stance Classification Part .....................................................9 3.3.1 Word Encoder.....................................................................10 3.3.2 Word Attention ..................................................................10 3.3.3 Sentence Attention.............................................................11 3.3.4 Model Training....................................................................11 3.4 Re-ranking ............................................................................11 4 Dataset Construction ..............................................................13 4.1 News Data Collection ............................................................13 4.2 Topic Selection .....................................................................13 4.3 Labeling Rules Definition.......................................................14 5 Experiments..............................................................................16 5.1 Experiment Data Setting ........................................................16 5.2 Evaluation Metric ...................................................................17 5.3 Basic Retrieval Model ............................................................17 5.4 Using Extra Resources...........................................................19 5.5 Effect of Relevance Feedback on Opinion Holder (FoOH) .....21 5.6 Visualization of Re-ranking ...................................................22 5.7 Other Experiments ................................................................22 6 Conclusions and Future Works ................................................24 6.1 Conclusions...........................................................................24 6.2 Future Works.........................................................................24 Bibliography ...............................................................................25 | |
dc.language.iso | en | |
dc.title | 新聞立場檢索之相關研究 | zh_TW |
dc.title | A Study on News Stance Retrieval | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳信希,林守德,陳柏琳 | |
dc.subject.keyword | 資訊檢索,立場分類,資訊擷取,意見探勘,關聯回饋, | zh_TW |
dc.subject.keyword | information retrieval,stance classification,information extraction,opinion mining,relevance feedback, | en |
dc.relation.page | 27 | |
dc.identifier.doi | 10.6342/NTU201901533 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-07-16 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
顯示於系所單位: | 資訊網路與多媒體研究所 |
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