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研究生： 杜惟真
To, Wei-Jen
論文名稱： 綜合知識庫與統計的字詞相似度預測方法
A Hybrid Learning-based Method for Estimating Word Similarity in Collocation Clustering
指導教授： 張俊盛
Chang, Jason
口試委員: 陳浩然
Chen, Hao-Jan
蘇豐文
Soo, Von-Wun
學位類別： 碩士
Master
系所名稱：
論文出版年： 2017
畢業學年度： 105
語文別： 英文
論文頁數： 43
中文關鍵詞： 搭配詞相似度 、相似字擷取 、機器學習
外文關鍵詞： Collocation Similarity, Synonym Retrieval, Machine Learning
相關次數： 點閱：2  下載：0 
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    • 我們提出了一套預測搭配詞相似度的機器學習方法，此方法是針對同樣中心詞的搭配詞進行相似度預測。我們的方法是從不同的語料庫中，綜合知識庫和統計方法產生訓練特徵值，以作為機器學習模型的訓練資料。我們分別從 WordNet 同義詞網結構與其相關資訊、網路規模的 n-gram 與中英雙語翻譯資料，產生考量中心詞之下的字詞相似特徵值。我們建構了一套搭配詞相似度的預測系統： ColloSim ，此系統可以預測牛津搭配字典的搭配詞相似度。初步的實驗評估顯示我們所提出的方法相較於最先進的方法有更好的表現。結果也顯示我們的方法在中心詞影響搭配詞相似度的情況下，能夠些微改進相似度預測準確性。

    We introduce a method for learning to estimate the similarity between collocations. In our approach, collocate pairs under certain headword are transformed into thesaurus-based and distributional similarity features from multiple sources. The method involves automatically generating similarity features, including WordNet-based features, n-gram based features, translation based features and headword-sensitive features for predicting the similarity between given collocate pairs. We present a similarity estimation prototype, ColloSim, that applies the method to collocations from a collocation dictionary. Evaluation on a set of collocates and their headword show that the method achieve reasonable good performance comparable to state-of-the-arts. Our method supports estimating the similarity of headword-sensitive collocations, resulting in additional improvement of the accuracy in predicting semantic similarity between collocate pairs.

    Abstract p.i 摘要 p.ii Acknowledgements p.iii Contents p.iv List of Figures p.vi List of Tables p.vii 1 Introduction p.1 2 Related Work p.7 2.1 Thesaurus-basedSimilarity p.8 2.2 DistributionalSimilarity p.9 3 Methodology p.11 3.1 ProblemStatement p.11 3.2 Similarity Features based on WordNet p.12 3.2.1 VerbalFeature p.14 3.2.2 AdjectivalFeatures p.15 3.3 Extract n-gram based Feature p.19 3.4 Similarity Features based onTranslations p.22 3.5 Headword-Sensitive Feature based on n-gram p.23 3.5.1 Retrieving and Filtering N-grams p.24 3.5.2 Transforming N-grams to N-frames p.26 3.5.3 Generating Context Vector p.27 3.5.4 Computing Headword-sensitive Similarity p.29 4 Experiment and Evaluation p.31 4.1 ExperimentalSetting p.31 4.1.1 Dataset p.32 4.1.2 Feature Generation Resources and Parameters p.33 4.1.3 Classification Model and Parameter settings p.34 4.2 SystemCompared p.34 4.3 EvaluationResults p.37 5 Conclusion and Future Work p.39 Reference p.40

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