TAILIEUCHUNG - Báo cáo khoa học: "A Novel Approach to Semantic Indexing Based on Concept"

This paper suggests the efficient indexing method based on a concept vector space that is capable of representing the semantic content of a document. The two information measure, namely the information quantity and the information ratio, are defined to represent the degree of the semantic importance within a document. The proposed method is expected to compensate the limitations of term frequency based methods by exploiting related lexical items. Furthermore, with information ratio, this approach is independent of document length. . | A Novel Approach to Semantic Indexing Based on Concept Bo-Yeong Kang Department of Computer Engineering Kyungpook National University 1370 Sangyukdong Pukgu Daegu Korea ROK comeng99@ Abstract This paper suggests the efficient indexing method based on a concept vector space that is capable of representing the semantic content of a document. The two information measure namely the information quantity and the information ratio are defined to represent the degree of the semantic importance within a document. The proposed method is expected to compensate the limitations of term frequency based methods by exploiting related lexical items. Furthermore with information ratio this approach is independent of document length. 1 Introduction To improve the unstable performance of a traditional keyword-based search a Web document should include both an index and index weight that represent the semantic content of the document. However most of the previous works on indexing and the weighting function which depend on statistical methods have limitations in extracting exact indexes Moens 2000 . The objective of this paper is to propose a method that extracts indexes efficiently and weights them according to their semantic importance degree in a document using concept vector space model. A document is regarded as a conglomerate concept that comprises by many concepts. Hence an n-dimensional concept vector space model is defined in such a way that a document is recognized as a vector in n-dimensional concept space. We used lexical chains for the extraction of concepts. With concept vectors and text vectors semantic indexes and their semantic importance degree are computed. Furthermore proposed indexing method had an advantage in being independent of document length because we regarded overall text information as a value 1 and represented each index weight by the semantic information ratio of overall text information. 2 Related Works Since index terms are not equally .

• Công nghệ - Môi trường
• A Novel Approach to Semantic
• Indexing Based on Concept
• Bo Yeong Kang
• 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