TAILIEUCHUNG - Báo cáo khoa học: "A Seed-driven Bottom-up Machine Learning Framework for Extracting Relations of Various Complexity"

A minimally supervised machine learning framework is described for extracting relations of various complexity. Bootstrapping starts from a small set of n-ary relation instances as “seeds”, in order to automatically learn pattern rules from parsed data, which then can extract new instances of the relation and its projections. We propose a novel rule representation enabling the composition of n-ary relation rules on top of the rules for projections of the relation. | A Seed-driven Bottom-up Machine Learning Framework for Extracting Relations of Various Complexity Feiyu Xu Hans Uszkoreit and Hong Li Language Technology Lab DFKI GmbH Stuhlsatzenhausweg 3 D-66123 Saarbruecken feiyu uszkoreit hongli @ Abstract A minimally supervised machine learning framework is described for extracting relations of various complexity. Bootstrapping starts from a small set of n-ary relation instances as seeds in order to automatically learn pattern rules from parsed data which then can extract new instances of the relation and its projections. We propose a novel rule representation enabling the composition of n-ary relation rules on top of the rules for projections of the relation. The compositional approach to rule construction is supported by a bottom-up pattern extraction method. In comparison to other automatic approaches our rules cannot only localize relation arguments but also assign their exact target argument roles. The method is evaluated in two tasks the extraction of Nobel Prize awards and management succession events. Performance for the new Nobel Prize task is strong. For the management succession task the results compare favorably with those of existing pattern acquisition approaches. 1 Introduction Information extraction IE has the task to discover n-tuples of relevant items entities belonging to an n-ary relation in natural language documents. One of the central goals of the ACE program1 is to develop a more systematically grounded approach to IE starting from elementary entities binary rela 1 http ace tions to n-ary relations such as events. Current semi- or unsupervised approaches to automatic pattern acquisition are either limited to a certain linguistic representation . subject-verb-object or only deal with binary relations or cannot assign slot filler roles to the extracted arguments or do not have good selection and filtering methods to handle the large number of tree patterns Riloff 1996 .

• Báo cáo khoa học
• A Seed driven Bottom up
• Machine Learning Framework
• Extracting Relations of Various Complexity
• 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
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• Thuộc tính về sự kiện
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• Mô hình ngôn ngữ Google Book N grams
• Mô hình ngôn ngữ KenLM
• Phương pháp làm mịn
• Tách từ tiếng Việt
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• Phân tích cú pháp
• Nghĩa từ vựng
• Phân giải nhập nhằng từ
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• Lexical chain
• Phản hồi thông tin
• Bài toán PTCP
• Cấu trúc ngữ pháp
• Phân tích cú pháp xác suất
• CKY kết hợp xác suất
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• Hình thái học