TAILIEUCHUNG - Báo cáo khoa học: "Grammar Approximation by Representative Sublanguage: A New Model for Language Learning"

We propose a new language learning model that learns a syntactic-semantic grammar from a small number of natural language strings annotated with their semantics, along with basic assumptions about natural language syntax. We show that the search space for grammar induction is a complete grammar lattice, which guarantees the uniqueness of the learned grammar. | Grammar Approximation by Representative Sublanguage A New Model for Language Learning Smaranda Muresan Institute for Advanced Computer Studies University of Maryland College Park MD 20742 USA smara@ Owen Rambow Center for Computational Learning Systems Columbia University New York NY 10027 USA rambow@ Abstract We propose a new language learning model that learns a syntactic-semantic grammar from a small number of natural language strings annotated with their semantics along with basic assumptions about natural language syntax. We show that the search space for grammar induction is a complete grammar lattice which guarantees the uniqueness of the learned grammar. 1 Introduction There is considerable interest in learning computational While much attention has focused on learning syntactic grammars either in a supervised or unsupervised manner recently there is a growing interest toward learning grammars parsers that capture semantics as well Bos et al. 2004 Zettlemoyer and Collins 2005 Ge and Mooney 2005 . Learning both syntax and semantics is arguably more difficult than learning syntax alone. In formal grammar learning theory it has been shown that learning from good examples or representative examples is more powerful than learning from all the examples Freivalds et al. 1993 . Haghighi and Klein 2006 show that using a handful of proto 1 This research was supported by the National Science Foundation under Digital Library Initiative Phase II Grant Number IIS-98-17434 Judith Klavans and Kathleen McKeown PIs . We would like to thank Judith Klavans for her contributions over the course of this research Kathy McKeown for her input and several anonymous reviewers for very useful feedback on earlier drafts of this paper. 832 types significantly improves over a fully unsupervised PCFG induction model their prototypes were formed by sequences of POS tags for example prototypical NPs were DT NN JJ NN . In this paper we present a new .

• Báo cáo khoa học
• Grammar Approximation
• Representative Sublanguage
• A New Model for Language Learning
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Finite state Approximation
• Constraint based Grammars
• Left corner Grammar Transforms
• 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