TAILIEUCHUNG - Báo cáo khoa học: "General-to-Specific Model Selection for Subcategorization Preference*"

This paper proposes a novel method for learning probability models of subcategorization preference of verbs. We consider the issues of case dependencies and noun class generalization in a uniform way by employing the maximum entropy modeling method. We also propose a new model selection algorithm which starts from the most general model and gradually examines more specific models. In the experimental evaluation, it is shown that both of the case dependencies and specific sense restriction selected by the proposed method contribute to improving the performance in subcategorization preference resolution. . | General-to-Specific Model Selection for Subcategorization Preference Takehito Utsuro and Takashi Miyata and Yuji Matsumoto Graduate School of Information Science Nara Institute of Science and Technology 8916-5 Takayama-cho Ikoma-shi Nara 630-0101 JAPAN E-mail URL http utsuro Abstract This paper proposes a novel method for learning probability models of subcategorization preference of verbs. We consider the issues of case dependencies and noun class generalization in a uniform way by employing the maximum entropy modeling method. We also propose a new model selection algorithm which starts from the most general model and gradually examines more specific models. In the experimental evaluation it is shown that both of the case dependencies and specific sense restriction selected by the proposed method contribute to improving the performance in subcategorization preference resolution. 1 Introduction In empirical approaches to parsing lexi-cal semantic collocation extracted from corpus has been proved to be quite useful for ranking parses in syntactic analysis. For example Mager-man 1995 Collins 1996 and Charniak 1997 proposed statistical parsing models which incorporated lexical semantic information. In their models syntactic and lexical semantic features are dependent on each other and are combined together. This paper also proposes a method of utilizing lexical semantic features for the purpose of applying them to ranking parses in syntactic analysis. However unlike the models of Magerman 1995 Collins 1996 and Char-niak 1997 we assume that syntactic and lex-ical semantic features are independent. Then we focus on extracting lexical semantic collocational knowledge of verbs which is useful in syntactic analysis. More specifically we propose a novel method for learning a probability model of subcategorization preference of verbs. In general when learning lexical semantic collocational knowledge of verbs from corpus it is .

• Báo cáo khoa học
• General to Specific
• Model Selection
• Subcategorization Preference
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Bài giảng Xử lý ngôn ngữ tự nhiên
• Ngôn ngữ tự nhiên
• Kỹ thuật lập trình
• Xử lý nhập nhằng ngữ nghĩa
• Xử lý ngữ nghĩa
• Xử lý ngôn ngữ
• Natural Language Processing
• Ngôn ngữ lập trình
• Nhập nhằng cấu trúc
• Tri thức về ngôn ngữ
• Mô hình n gram
• Dịch máy
• Phương pháp dịch máy
• Hiểu ngôn ngữ
• Phân tích ngữ nghĩa
• Phân loại tin tự động
• Biểu diễn vị từ
• Thuộc tính về sự kiện
• Thuộc tính về sự kiện
• Mô hình ngôn ngữ Google Book N grams
• Mô hình ngôn ngữ KenLM
• Phương pháp làm mịn
• Tách từ tiếng Việt
• Gán nhãn từ loại
• Phân tích cú pháp
• Nghĩa từ vựng
• Phân giải nhập nhằng từ
• Phân loại văn bản
• Lexical chain
• Phản hồi thông tin
• Bài toán PTCP
• Cấu trúc ngữ pháp
• Phân tích cú pháp xác suất
• CKY kết hợp xác suất
• Văn phạm phi ngữ cảnh xác suất
• Hình thái học