英文介系詞片語定位與英文介系詞推薦

Attachment of English Prepositional Phrases and Suggestions of English Prepositions

本研究專注於介系詞相關的二個議題：介系詞片語定位與介系詞推薦。我們將這二個議題抽象化為一個決策問題，並提出一個一般化的解決方法。這二個問題共通的部分在於動詞片語；一個簡單的動詞片語含有最重要的四個中心詞（headword）：動詞、名詞一、介系詞和名詞二。由這四個中心詞做為出發點，透過WordNet做階層式的選擇，在大量的案例中尋找語義上共通的部分，再利用機器學習的方法建構一般化的模型。此外，針對介系詞片語定位問題，我們挑選實驗具挑戰性的介系詞做實驗。藉由使用真實生活語料，我們的方法處理介系詞片語定位的問題，可以有不錯的表現；而對於介系詞推薦的問題，我們的方法難有全面比較的對象，但精準度可達到47.76%。本研究發現，高層次的語義可以使分類器有不錯的分類效果，但透過階層式的語義選擇能使分類效果更佳。這顯示我們確實可以透過語義歸納一套準則，用於二個介系詞的議題。相信成果在未來會對機器翻譯與文本校對的相關研究有所價值。

This paper focuses on problems of attachment of prepositional phrases (PPs) and problems of prepositional suggestions. We transform the problems of PPs attachment and prepositional suggestions into an abstract model, and apply the same computational procedures to solve these two problems. The common model features four headwords, i.e., the verb, the first noun, the preposition, and the second noun in the prepositional phrases. Our methods consider the semantic features of the headwords in WordNet to train classification models, and apply the learned models for tackling the attachment and suggestion problems. This exploration of PP attachment problems is special in that only those PPs that are almost equally possible to attach to the verb and the first noun were used in the study. The proposed models consider only four headwords to achieve satisfactory performances. This study reconfirms that semantic information is instrument for both PP attachment and prepositional suggestions.