TAILIEUCHUNG - Báo cáo khoa học: "Parsing Noun Phrase Structure with CCG"

Statistical parsing of noun phrase (NP) structure has been hampered by a lack of goldstandard data. This is a significant problem for CCGbank, where binary branching NP derivations are often incorrect, a result of the automatic conversion from the Penn Treebank.(N (N/N lung) (N (N/N cancer) (N deaths) ) )This structure is correct for most English NPs and is the best solution that doesn’t require manual reannotation. However, the resulting derivations often contain errors. This can be seen in the previous exWe correct these errors in CCGbank using a gold-standard corpus of NP structure, resultample, . | Parsing Noun Phrase Structure with CCG David Vadas and James R. Curran School of Information Technologies University of Sydney NSW 2006 Australia dvadasl james @ Abstract Statistical parsing of noun phrase NP structure has been hampered by a lack of gold-standard data. This is a significant problem for CCGbank where binary branching NP derivations are often incorrect a result of the automatic conversion from the Penn Treebank. We correct these errors in CCGbank using a gold-standard corpus of NP structure resulting in a much more accurate corpus. We also implement novel NER features that generalise the lexical information needed to parse NPs and provide important semantic information. Finally evaluating against DepBank demonstrates the effectiveness of our modified corpus and novel features with an increase in parser performance of . 1 Introduction Internal noun phrase np structure is not recovered by a number of widely-used parsers . Collins 2003 . This is because their training data the Penn Treebank Marcus et al. 1993 does not fully annotate NP structure. The flat structure described by the Penn Treebank can be seen in this example NP NN lung NN cancer NNS deaths CCGbank Hockenmaier and Steedman 2007 is the primary English corpus for Combinatory Categorial Grammar ccg Steedman 2000 and was created by a semi-automatic conversion from the Penn Treebank. However CCG is a binary branching grammar and as such cannot leave N P structure underspecified. Instead all NPs were made rightbranching as shown in this example N N N lung N N N cancer N deaths This structure is correct for most English NPs and is the best solution that doesn t require manual reannotation. However the resulting derivations often contain errors. This can be seen in the previous example where lung cancer should form a constituent but does not. The first contribution of this paper is to correct these CCGbank errors. We apply an automatic conversion process using the .

• Báo cáo khoa học
• Parsing Noun Phrase
• Structure with CCG
• David Vadas and James R
• Curran
• báo cáo khoa học
• báo cáo ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Effects of Noun Phrase Bracketing
• Dependency Parsing
• Machine Translation
• ngôn ngữ tự nhiên
• Coordinate Noun Phrase Disambiguation
• Generative Parsing Model
• Deirdre Hogan
• mô hình ngôn ngữ
• 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