TAILIEUCHUNG - Báo cáo khoa học: "Hedge classification in biomedical texts with a weakly supervised selection of keywords"

Since facts or statements in a hedge or negated context typically appear as false positives, the proper handling of these language phenomena is of great importance in biomedical text mining. In this paper we demonstrate the importance of hedge classification experimentally in two real life scenarios, namely the ICD9-CM coding of radiology reports and gene name Entity Extraction from scientific texts. We analysed the major differences of speculative language in these tasks and developed a maxent-based solution for both the free text and scientific text processing tasks. . | Hedge classification in biomedical texts with a weakly supervised selection of keywords Gyorgy Szarvas Research Group on Artificial Intelligence Hungarian Academy of Sciences University of Szeged HU-6720 Szeged Hungary szarvas@ Abstract Since facts or statements in a hedge or negated context typically appear as false positives the proper handling of these language phenomena is of great importance in biomedical text mining. In this paper we demonstrate the importance of hedge classification experimentally in two real life scenarios namely the ICD-9-CM coding of radiology reports and gene name Entity Extraction from scientific texts. We analysed the major differences of speculative language in these tasks and developed a maxent-based solution for both the free text and scientific text processing tasks. Based on our results we draw conclusions on the possible ways of tackling speculative language in biomedical texts. 1 Introduction The highly accurate identification of several regularly occurring language phenomena like the speculative use of language negation and past tense temporal resolution is a prerequisite for the efficient processing of biomedical texts. In various natural language processing tasks relevant statements appearing in a speculative context are treated as false positives. Hedge detection seeks to perform a kind of semantic filtering of texts that is it tries to separate factual statements from speculative uncertain ones. Hedging in biomedical NLP To demonstrate the detrimental effects of speculative language on biomedical NLP tasks we will consider two inherently different sample tasks namely the ICD-9-CM coding of radiology records and gene information extraction from biomedical scientific texts. The general features of texts used in these tasks differ significantly from each other but both tasks require the exclusion of uncertain or speculative items from processing. Gene Name and interaction extraction from scientific .

• Báo cáo khoa học
• Hedge classification
• biomedical texts
• weakly supervised selection of keywords
• báo cáo khoa học
• báo cáo ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Weakly Supervised Learning
• Scientific Literature
• mô hình ngôn ngữ
• 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