TAILIEUCHUNG - Báo cáo khoa học: "Mapping between Compositional Semantic Representations and Lexical Semantic Resources: Towards Accurate Deep Semantic Parsing"

This paper introduces a machine learning method based on bayesian networks which is applied to the mapping between deep semantic representations and lexical semantic resources. A probabilistic model comprising Minimal Recursion Semantics (MRS) structures and lexicalist oriented semantic features is acquired. Lexical semantic roles enriching the MRS structures are inferred, which are useful to improve the accuracy of deep semantic parsing. | Mapping between Compositional Semantic Representations and Lexical Semantic Resources Towards Accurate Deep Semantic Parsing Sergio Roaft Valia Kordonif and Yi Zhang Dept. of Computational Linguistics Saarland University Germany German Research Center for Artificial Intelligence DFKI GmbH Dept. of Computer Science University of Freiburg Germany sergior kordoni yzhang @ Abstract This paper introduces a machine learning method based on bayesian networks which is applied to the mapping between deep semantic representations and lexical semantic resources. A probabilistic model comprising Minimal Recursion Semantics MRS structures and lexicalist oriented semantic features is acquired. Lexical semantic roles enriching the MRS structures are inferred which are useful to improve the accuracy of deep semantic parsing. Verb classes inference was also investigated which together with lexical semantic information provided by VerbNet and PropBank resources can be substantially beneficial to the parse disambiguation task. 1 Introduction Recent studies of natural language parsing have shown a clear and steady shift of focus from pure syntactic analyses to more semantically informed structures. As a result we have seen an emerging interest in parser evaluation based on more theoryneutral and semantically informed representations such as dependency structures. Some approaches have even tried to acquire semantic representations without full syntactic analyses. The so-called shallow semantic parsers build basic predicate-argument structures or label semantic roles that reveal the partial meaning of sentences Carreras and Marquez 2005 . Manually annotated lexical semantic resources like PropBank Palmer et al. 2005 Verb-Net Kipper-Schuler 2005 or FrameNet Baker et al. 1998 are usually used as gold standards for training and evaluation of such systems. In the meantime various existing parsing systems are also adapted to provide semantic information in their outputs. The .

• Báo cáo khoa học
• Mapping between Compositional Semantic Representations
• Lexical Semantic Resources
• Towards Accurate Deep Semantic Parsing
• báo cáo khoa học
• báo cáo 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 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