TAILIEUCHUNG - Báo cáo khoa học: "Extracting Causal Knowledge from a Medical Database Using Graphical Patterns"

This paper reports the first part of a project that aims to develop a knowledge extraction and knowledge discovery system that extracts causal knowledge from textual databases. In this initial study, we develop a method to identify and extract cause-effect information that is explicitly expressed in medical abstracts in the Medline database. A set of graphical patterns were constructed that indicate the presence of a causal relation in sentences, and which part of the sentence represents the cause and which part represents the effect. . | Extracting Causal Knowledge from a Medical Database Using Graphical Patterns Christopher . Khoo Syin Chan and Yun Niu Centre for Advanced Information Systems School of Computer Engineering Blk N4 Rm2A-32 Nanyang Avenue Nanyang Technological University Singapore 639798 assgkhoo@ asschan@ niuyun@ Abstract This paper reports the first part of a project that aims to develop a knowledge extraction and knowledge discovery system that extracts causal knowledge from textual databases. In this initial study we develop a method to identify and extract cause-effect information that is explicitly expressed in medical abstracts in the Medline database. A set of graphical patterns were constructed that indicate the presence of a causal relation in sentences and which part of the sentence represents the cause and which part represents the effect. The patterns are matched with the syntactic parse trees of sentences and the parts of the parse tree that match with the slots in the patterns are extracted as the cause or the effect. 1 Introduction Vast amounts of textual documents and databases are now accessible on the Internet and the World Wide Web. However it is very difficult to retrieve useful information from this huge disorganized storehouse. Programs that can identify and extract useful information and relate and integrate information from multiple sources are increasingly needed. The World Wide Web presents tremendous opportunities for developing knowledge extraction and knowledge discovery programs that automatically extract and acquire knowledge about a domain by integrating information from multiple sources. New knowledge can be discovered by relating disparate pieces of information and by infer-encing from the extracted knowledge. This paper reports the first phase of a project to develop a knowledge extraction and knowl edge discovery system that focuses on causal knowledge. A system is being developed to identify and extract .

• Báo cáo khoa học
• Extracting Causal Knowledge
• a Medical Database
• Graphical Patterns
• 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