TAILIEUCHUNG - Báo cáo khoa học: "Corpus-Based Induction of Syntactic Structure: Models of Dependency and Constituency"

We present a generative model for the unsupervised learning of dependency structures. We also describe the multiplicative combination of this dependency model with a model of linear constituency. The product model outperforms both components on their respective evaluation metrics, giving the best published figures for unsupervised dependency parsing and unsupervised constituency parsing. We also demonstrate that the combined model works and is robust cross-linguistically, being able to exploit either attachment or distributional regularities that are salient in the data. . | Corpus-Based Induction of Syntactic Structure Models of Dependency and Constituency Dan Klein Computer Science Department Stanford University Stanford CA 94305-9040 klein@ Christopher D. Manning Computer Science Department Stanford University Stanford CA 94305-9040 manning@ Abstract We present a generative model for the unsupervised learning of dependency structures. We also describe the multiplicative combination of this dependency model with a model of linear constituency. The product model outperforms both components on their respective evaluation metrics giving the best published figures for unsupervised dependency parsing and unsupervised constituency parsing. We also demonstrate that the combined model works and is robust cross-linguistically being able to exploit either attachment or distributional regularities that are salient in the data. 1 Introduction The task of statistically inducing hierarchical syntactic structure over unannotated sentences of natural language has received a great deal of attention Carroll and Charniak 1992 Pereira and Schabes 1992 Brill 1993 Stolcke and Omohundro 1994 . Researchers have explored this problem for a variety of reasons to argue empirically against the poverty of the stimulus Clark 2001 to use induction systems as a first stage in constructing large treebanks van Zaanen 2000 to build better language models Baker 1979 Chen 1995 and to examine cognitive issues in language learning Solan et al. 2003 . An important distinction should be drawn between work primarily interested in the weak generative capacity of models where modeling hierarchical structure is only useful insofar as it leads to improved models over observed structures Baker 1979 Chen 1995 and work interested in the strong generative capacity of models where the unobserved structure itself is evaluated van Zaa-nen 2000 Clark 2001 Klein and Manning 2002 . This paper falls into the latter category we will be inducing models of .

• Báo cáo khoa học
• Corpus Based Induction of Syntactic Structure
• Models of Dependency and Constituency
• Dan Klein
• 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