TAILIEUCHUNG - Báo cáo khoa học: "Classifying Semantic Relations in Bioscience Texts"

A crucial step toward the goal of automatic extraction of propositional information from natural language text is the identification of semantic relations between constituents in sentences. We examine the problem of distinguishing among seven relation types that can occur between the entities “treatment” and “disease” in bioscience text, and the problem of identifying such entities. We compare five generative graphical models and a neural network, using lexical, syntactic, and semantic features, finding that the latter help achieve high classification accuracy. . | Classifying Semantic Relations in Bioscience Texts Barbara Rosario SIMS UC Berkeley Berkeley CA 94720 rosario@ Marti A. Hearst SIMS UC Berkeley Berkeley CA 94720 hearst@ Abstract A crucial step toward the goal of automatic extraction of propositional information from natural language text is the identification of semantic relations between constituents in sentences. We examine the problem of distinguishing among seven relation types that can occur between the entities treatment and disease in bioscience text and the problem of identifying such entities. We compare five generative graphical models and a neural network using lexical syntactic and semantic features finding that the latter help achieve high classification accuracy. 1 Introduction The biosciences literature is rich complex and continually growing. The National Library of Medicine s MEDLINE database1 contains bibliographic citations and abstracts from more than 4 600 biomedical journals and an estimated half a million new articles are added every year. Much of the important late-breaking bioscience information is found only in textual form and so methods are needed to automatically extract semantic entities and the relations between them from this text. For example in the following sentences hepatitis and its variants which are DISEASES are found in different semantic relationships with various treatments 1 http pubs factsheets 1 Effect of interferon on hepatitis B 2 A two-dose combined hepatitis A and B vaccine would facilitate immunization programs 3 These results suggest that con A-induced hepatitis was ameliorated by pretreatment with TJ-135. In 1 there is an unspecified effect of the treatment interferon on hepatitis B. In 2 the vaccine prevents hepatitis A and B while in 3 hepatitis is cured by the treatment TJ-135. We refer to this problem as Relation Classification. A related task is Role Extraction also called in the literature .

• Báo cáo khoa học
• Classifying Semantic Relations
• Bioscience Texts
• Barbara Rosario
• 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