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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳信希(Hsin-Hsi Chen) | |
| dc.contributor.author | Hao Ke | en |
| dc.contributor.author | 葛浩 | zh_TW |
| dc.date.accessioned | 2021-06-15T12:31:12Z | - |
| dc.date.available | 2016-08-24 | |
| dc.date.copyright | 2016-08-24 | |
| dc.date.issued | 2016 | |
| dc.date.submitted | 2016-08-04 | |
| dc.identifier.citation | Agirre, E., Alfonseca, E., Hall, K., Kravalova, J., Paşca, M., & Soroa, A. (2009, May). A
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/50159 | - |
| dc.description.abstract | 本文提出三種不同的方法來處理計算語義關聯度的問題：一、去除或調整GloVe詞向量內之不正常維度來提高效能；二、利用WordNet的距離資訊與詞向量做線性組合；三、用詞向量以及十二個從WordNet擷取出來的資訊作為SVR的特徵做監督式學習。
本文在六個評測基準資料集進行了實驗，以皮爾森相關係數與斯皮爾曼相關係數計算本文的方法產生之結果與正確標記之間的相關程度，並且與三個近期提出的計算語義關聯度方法做比較。實驗結果顯示，本文的方法在多組評測基準資料集上超越了以上三個近期提出的方法。 | zh_TW |
| dc.description.abstract | In this thesis, we propose three different approaches to measure the semantic relatedness: (1) Boost the performance of GloVe word embedding by removing ortransforming abnormal dimensions. (2) Linearly combines the path information extracted from WordNet and the word embedding. (3) Utilize word embedding and twelve linguisticinformation extracted from WordNet as features for support vector regression.
We conduct our experiments on six benchmark data sets. The evaluation measurecomputes the Pearson and Spearman correlation between the output of our methods and the ground truth. We report our results together with three state-of-the-art approaches. Theexperimental results show that our methods outperform the state-of-the-art approaches in most of the benchmark data sets. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T12:31:12Z (GMT). No. of bitstreams: 1 ntu-105-R03922118-1.pdf: 1811568 bytes, checksum: 82370f327d85e8279f5aebf35bc4529a (MD5) Previous issue date: 2016 | en |
| dc.description.tableofcontents | 口試委員審定書......................................................................................................................i
誌謝.........................................................................................................................................ii 摘要.......................................................................................................................................iii Abstract..................................................................................................................................iv 圖目錄..................................................................................................................................vii 表目錄.................................................................................................................................viii 第一章 緒論...........................................................................................................................1 第二章 相關研究...................................................................................................................3 第三章 使用資源...................................................................................................................6 3.1 WordNet................................................................................................................6 3.2 測試資料集...........................................................................................................8 3.2.1 RG-65.................................................................................................................8 3.2.2 WordSim-353.....................................................................................................8 3.2.3 YP-130..............................................................................................................10 3.2.4 MEN.................................................................................................................10 3.2.5 SimLex-999......................................................................................................11 3.2.6 Rare Words.......................................................................................................12 第四章 語義關聯度計算方法.............................................................................................15 4.1 方法一：詞向量.................................................................................................15 4.1.1 GloVe詞向量中之不正常維度...........................................................15 4.1.2 去除及調整不正常維度......................................................................19 4.2 方法二：詞向量與WordNet.............................................................................21 4.3 方法三：詞向量與SVR....................................................................................22 第五章 實驗.........................................................................................................................24 5.1 皮爾森相關係數與斯皮爾曼相關係數.............................................................24 5.2 比較對象.............................................................................................................25 5.3 方法一.................................................................................................................25 5.4 方法二.................................................................................................................30 5.5 方法三.................................................................................................................32 5.6 WordNet特徵分析.............................................................................................34 第六章 結論.........................................................................................................................37 參考文獻...............................................................................................................................38 | |
| dc.language.iso | zh-TW | |
| dc.title | 數種結合詞向量與字典資源之方法用於字義相似度測量 | zh_TW |
| dc.title | Some approaches of combining word embedding and lexical
resource for semantic relateness mesurement | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 104-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 鄭卜壬(Pu-Jen Cheng),蔡宗翰(Tzong-Han Tsai),蔡銘峰(Ming-Feng Tsai) | |
| dc.subject.keyword | 語義關聯度,詞向量,WordNet,GloVe,Word2Vec, | zh_TW |
| dc.subject.keyword | semantic relatedness,word embedding,WordNet,GloVe,Word2Vec, | en |
| dc.relation.page | 41 | |
| dc.identifier.doi | 10.6342/NTU201601910 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2016-08-04 | |
| 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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