tailieunhanh - Báo cáo khoa học: "A Metric-based Framework for Automatic Taxonomy Induction"

This paper presents a novel metric-based framework for the task of automatic taxonomy induction. The framework incrementally clusters terms based on ontology metric, a score indicating semantic distance; and transforms the task into a multi-criteria optimization based on minimization of taxonomy structures and modeling of term abstractness. It combines the strengths of both lexico-syntactic patterns and clustering through incorporating heterogeneous features. The flexible design of the framework allows a further study on which features are the best for the task under various conditions. . | A Metric-based Framework for Automatic Taxonomy Induction Hui Yang Language Technologies Institute School of Computer Science Carnegie Mellon University huiyang@ Jamie Callan Language Technologies Institute School of Computer Science Carnegie Mellon University callan@ Abstract This paper presents a novel metric-based framework for the task of automatic taxonomy induction. The framework incrementally clusters terms based on ontology metric a score indicating semantic distance and transforms the task into a multi-criteria optimization based on minimization of taxonomy structures and modeling of term abstractness. It combines the strengths of both lexico-syntactic patterns and clustering through incorporating heterogeneous features. The flexible design of the framework allows a further study on which features are the best for the task under various conditions. The experiments not only show that our system achieves higher F1-measure than other state-of-the-art systems but also reveal the interaction between features and various types of relations as well as the interaction between features and term abstractness. 1 Introduction Automatic taxonomy induction is an important task in the fields of Natural Language Processing Knowledge Management and Semantic Web. It has been receiving increasing attention because semantic taxonomies such as WordNet Fellbaum 1998 play an important role in solving knowledge-rich problems including question answering Harabagiu et al. 2003 and textual entailment Geffet and Dagan 2005 . Nevertheless most existing taxonomies are manually created at great cost. These taxonomies are rarely complete it is difficult to include new terms in them from emerging or rapidly changing domains. Moreover manual taxonomy construction is time-consuming which may make it unfeasible for specialized domains and personalized tasks. Automatic taxonomy induction is a solution to augment existing resources and to pro duce new taxonomies for such .

• Báo cáo khoa học
• Metric based Framework
• Automatic Taxonomy Induction
• Hui Yang
• báo cáo khoa học
• báo cáo 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 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