tailieunhanh - Báo cáo khoa học: "Mining metalinguistic activity in corpora to create lexical resources using Information Extraction techniques: the MOP system"

database maintained by the National Library of Medicine1 (NLM), which incorporates around 40,000 Health Sciences papers each month. Researchers depend on these electronic resources to keep abreast of their rapidly changing field. In order to maintain and update vital indexing references such as the Unified Medical Language System (UMLS) resources, the MeSH and SPECIALIST vocabularies, the NLM staff needs to review 400,000 highly-technical papers each year. Clearly, neology detection, terminological information update and other tasks can benefit from applications that automatically search text for information, ., when a new term is introduced or an existing one is modified. | Mining metalinguistic activity in corpora to create lexical resources using Information Extraction techniques the MOP system Carlos Rodríguez Penagos Language Engineering Group Engineering Institute UNAM Ciudad Universitaria . 70-472 Coyoacán 04510 Mexico City México CRodriguezP@ Abstract This paper describes and evaluates MOP an IE system for automatic extraction of metalinguistic information from technical and scientific documents. We claim that such a system can create special databases to bootstrap compilation and facilitate update of the huge and dynamically changing glossaries knowledge bases and ontologies that are vital to modern-day research. 1 Introduction Availability of large-scale corpora has made it possible to mine specific knowledge from free or semi-structured text resulting in what many consider by now a reasonably mature NLP technology. Extensive research in Information Extraction IE techniques especially with the series of Message Understanding Conferences of the nineties has focused on tasks such as creating and updating databases of corporate join ventures or terrorist and guerrilla attacks while the ACQUILEX project used similar methods for creating lexical databases using the highly structured environment of machine-readable dictionary entries and other resources. Gathering knowledge from unstructured text often requires manually crafting knowledgeengineering rules both complex and deeply dependent of the domain at hand although some successful experiences using learning algorithms have been reported Fisher et al. 1995 Chieu et al. 2003 . Although mining specific semantic relations and subcategorization information from free-text has been successfully carried out in the past Hearst 1999 Manning 1993 automatically extracting lexical resources including terminological definitions from text in special domains has been a field less explored but recent experiences Klavans et al. 2001 Rodríguez 2001 Cartier 1998 show that .

• Báo cáo khoa học
• Mining metalinguistic activity
• corpora to create lexical resources
• Information Extraction techniques
• the MOP system
• 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