TAILIEUCHUNG - Báo cáo khoa học: "Instance Splitting Strategies for Dependency Relation-based Information Extraction"

Information Extraction (IE) is a fundamental technology for NLP. Previous methods for IE were relying on co-occurrence relations, soft patterns and properties of the target (for example, syntactic role), which result in problems of handling paraphrasing and alignment of instances. Our system ARE (Anchor and Relation) is based on the dependency relation model and tackles these problems by unifying entities according to their dependency relations, which we found to provide more invariant relations between entities in many cases. . | ARE Instance Splitting Strategies for Dependency Relation-based Information Extraction Mstislav Maslennikov Hai-Kiat Goh Tat-Seng Chua Department of Computer Science School of Computing National University of Singapore maslenni gohhaiki chuats @ Abstract Information Extraction IE is a fundamental technology for NLP. Previous methods for IE were relying on co-occurrence relations soft patterns and properties of the target for example syntactic role which result in problems of handling paraphrasing and alignment of instances. Our system ARE Anchor and Relation is based on the dependency relation model and tackles these problems by unifying entities according to their dependency relations which we found to provide more invariant relations between entities in many cases. In order to exploit the complexity and characteristics of relation paths we further classify the relation paths into the categories of easy average and hard and utilize different extraction strategies based on the characteristics of those categories. Our extraction method leads to improvement in performance by 3 and 6 for MUC4 and MUC6 respectively as compared to the state-of-art IE systems. 1 Introduction Information Extraction IE is one of the fundamental problems of natural language processing. Progress in IE is important to enhance results in such tasks as Question Answering Information Retrieval and Text Summarization. Multiple efforts in MUC series allowed IE systems to achieve nearhuman performance in such domains as biological Humphreys et al. 2000 terrorism Kaufmann 1992 Kaufmann 1993 and management succession Kaufmann 1995 . The IE task is formulated for MUC series as filling of several predefined slots in a template. The terrorism template consists of slots Perpetrator Victim and Target the slots in the management succession template are Org PersonIn PersonOut and Post. We decided to choose both terrorism and management succession domains from MUC4 and MUC6 respectively in .

• Báo cáo khoa học
• Instance Splitting Strategies
• Dependency Relation based
• Information Extraction
• 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