TAILIEUCHUNG - Báo cáo khoa học: "Discovering Relations among Named Entities from Large Corpora"

Discovering the significant relations embedded in documents would be very useful not only for information retrieval but also for question answering and summarization. Prior methods for relation discovery, however, needed large annotated corpora which cost a great deal of time and effort. We propose an unsupervised method for relation discovery from large corpora. The key idea is clustering pairs of named entities according to the similarity of context words intervening between the named entities. . | Discovering Relations among Named Entities from Large Corpora Satoshi Sekine and Ralph Grishman Dept. of Computer Science New York University 715 Broadway 7th floor New York NY 10003 . sekine grishman @ Takaaki Hasegawa Cyberspace Laboratories Nippon Telegraph and Telephone Corporation 1-1 Hikarinooka Yokosuka Kanagawa 239-0847 Japan Abstract Discovering the significant relations embedded in documents would be very useful not only for information retrieval but also for question answering and summarization. Prior methods for relation discovery however needed large annotated corpora which cost a great deal of time and effort. We propose an unsupervised method for relation discovery from large corpora. The key idea is clustering pairs of named entities according to the similarity of context words intervening between the named entities. Our experiments using one year of newspapers reveals not only that the relations among named entities could be detected with high recall and precision but also that appropriate labels could be automatically provided for the relations. 1 Introduction Although Internet search engines enable us to access a great deal of information they cannot easily give us answers to complicated queries such as a list of recent mergers and acquisitions of companies or current leaders of nations from all over the world . In order to find answers to these types of queries we have to analyze relevant documents to collect the necessary information. If many relations such as Company A merged with Company B embedded in those documents could be gathered and structured automatically it would be very useful not only for information retrieval but also for question answering and summarization. Information Extraction provides methods for extracting information such as particular events and relations between entities from text. However it is domain dependent and it could not give answers to those types of queries from Web

• Báo cáo khoa học
• Discovering Relations
• Named Entities from Large Corpora
• Takaaki Hasegawa Satoshi Sekine and Ralph Grishman
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Discovering asymmetric
• entailment relations between verbs
• selectional preferences
• 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