TAILIEUCHUNG - Báo cáo khoa học: "Discriminative Reranking for Semantic Parsing"

Semantic parsing is the task of mapping natural language sentences to complete formal meaning representations. The performance of semantic parsing can be potentially improved by using discriminative reranking, which explores arbitrary global features. In this paper, we investigate discriminative reranking upon a baseline semantic parser, S CISSOR, where the composition of meaning representations is guided by syntax. We examine if features used for syntactic parsing can be adapted for semantic parsing by creating similar semantic features based on the mapping between syntax and semantics. We report experimental results on two real applications, an interpreter for coaching instructions in robotic. | Discriminative Reranking for Semantic Parsing Ruifang Ge Raymond J. Mooney Department of Computer Sciences University of Texas at Austin Austin TX 78712 grf mooney @ Abstract Semantic parsing is the task of mapping natural language sentences to complete formal meaning representations. The performance of semantic parsing can be potentially improved by using discriminative reranking which explores arbitrary global features. In this paper we investigate discriminative reranking upon a baseline semantic parser Scissor where the composition of meaning representations is guided by syntax. We examine if features used for syntactic parsing can be adapted for semantic parsing by creating similar semantic features based on the mapping between syntax and semantics. We report experimental results on two real applications an interpreter for coaching instructions in robotic soccer and a naturallanguage database interface. The results show that reranking can improve the performance on the coaching interpreter but not on the database interface. 1 Introduction A long-standing challenge within natural language processing has been to understand the meaning of natural language sentences. In comparison with shallow semantic analysis tasks such as wordsense disambiguation Ide and Jeaneronis 1998 and semantic role labeling Gildea and Jurafsky 2002 Carreras and Marquez 2005 which only partially tackle this problem by identifying the meanings of target words or finding semantic roles of predicates semantic parsing Kate et al. 2005 Ge and Mooney 2005 Zettlemoyer and Collins 2005 pursues a more ambitious goal - mapping natural language sentences to complete formal meaning representations MRs where the meaning of each part of a sentence is analyzed including noun phrases verb phrases negation quantifiers and so on. Semantic parsing enables logic reasoning and is critical in many practical tasks such as speech understanding Zue and Glass 2000 question answering Lev et al. 2004 and

• Báo cáo khoa học
• Discriminative Reranking
• Semantic Parsing
• Ruifang Ge Raymond J
• Mooney
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Concept to text Generation
• Ioannis Konstas and Mirella Lapata
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• Forest Reranking
• Discriminative Parsing
• Non Local Features
• Coarse to fine n best parsing
• MaxEnt discriminative reranking
• Eugene Charniak and Mark Johnson Brown
• Bài giảng Xử lý 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