tailieunhanh - Báo cáo khoa học: "Analysis System of Speech Acts and Discourse Structures Using Maximum Entropy Model*"

We propose a statistical dialogue analysis model to determine discourse structures as well as speech acts using maximum entropy model. The model can automatically acquire probabilistic discourse knowledge from a discourse tagged corpus to resolve ambiguities. We propose the idea of tagging discourse segment boundaries to represent the structural information of discourse. Using this representation we can effectively combine speech act analysis and discourse structure analysis in one framework. | Analysis System of Speech Acts and Discourse Structures Using Maximum Entropy Model Won Seug Choi Jeong-Mi Cho and Jungyun Seo Dept of Computer Science Sogang University Sinsu-dong 1 Mapo-gu Seoul Korea 121-742 dolhana jmcho @ seojy@ Abstract We propose a statistical dialogue analysis model to determine discourse structures as well as speech acts using maximum entropy model. The model can automatically acquire probabilistic discourse knowledge from a discourse tagged corpus to resolve ambiguities. We propose the idea of tagging discourse segment boundaries to represent the structural information of discourse. Using this representation we can effectively combine speech act analysis and discourse structure analysis in one framework. Introduction To understand a natural language dialogue a computer system must be sensitive to the speaker s intentions indicated through utterances. Since identifying the speech acts of utterances is very important to identify speaker s intentions it is an essential part of a dialogue analysis system. It is difficult however to infer the speech act from a surface utterance since an utterance may represent more than one speech act according to the context. Most works done in the past on the dialogue analysis has analyzed speech acts based on knowledge such as recipes for plan inference and domain specific knowledge Litman 1987 Caberry 1989 Hinkelman 1990 Lambert 1991 Lambert 1993 Lee 1998 . Since these knowledge-based models depend on costly hand-crafted knowledge these models are difficult to be scaled up and expanded to other domains. Recently machine learning models using a discourse tagged corpus are utilized to analyze speech acts in order to overcome such problems Nagata 1994a Nagata 1994b Reithinger 1997 Lee 1997 Samuel 1998 . Machine learning offers promise as a means of associating features of utterances with particular speech acts since computers can automatically analyze large quantities of .

• Báo cáo khoa học
• Analysis System of Speech Acts
• Discourse Structures
• Maximum Entropy Model
• 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