TAILIEUCHUNG - Báo cáo khoa học: "Learning Noun Phrase Anaphoricity to Improve Coreference Resolution: Issues in Representation and Optimization"

Knowledge of the anaphoricity of a noun phrase might be profitably exploited by a coreference system to bypass the resolution of non-anaphoric noun phrases. Perhaps surprisingly, recent attempts to incorporate automatically acquired anaphoricity information into coreference systems, however, have led to the degradation in resolution performance. This paper examines several key issues in computing and using anaphoricity information to improve learning-based coreference systems. In particular, we present a new corpus-based approach to anaphoricity determination. Experiments on three standard coreference data sets demonstrate the effectiveness of our approach. . | Learning Noun Phrase Anaphoricity to Improve Coreference Resolution Issues in Representation and Optimization Vincent Ng Department of Computer Science Cornell University Ithaca NY 14853-7501 yung@ Abstract Knowledge of the anaphoricity of a noun phrase might be profitably exploited by a coreference system to bypass the resolution of non-anaphoric noun phrases. Perhaps surprisingly recent attempts to incorporate automatically acquired anaphoricity information into coreference systems however have led to the degradation in resolution performance. This paper examines several key issues in computing and using anaphoricity information to improve learning-based coreference systems. In particular we present a new corpus-based approach to anaphoricity determination. Experiments on three standard coreference data sets demonstrate the effectiveness of our approach. 1 Introduction Noun phrase coreference resolution the task of determining which noun phrases NPs in a text refer to the same real-world entity has long been considered an important and difficult problem in natural language processing. Identifying the linguistic constraints on when two NPs can co-refer remains an active area of research in the community. One significant constraint on coreference the non-anaphoricity constraint specifies that a non-anaphoric NP cannot be coreferent with any of its preceding NPs in a given text. Given the potential usefulness of knowledge of non- anaphoricity for coreference resolution anaphoricity determination has been studied fairly extensively. One common approach involves the design of heuristic rules to identify specific types of non- anaphoric NPs such as pleonastic pronouns . Paice and Husk 1987 Lappin and Le-ass 1994 Kennedy and Boguraev 1996 Den-ber 1998 and definite descriptions . Vieira and Poesio 2000 . More recently the problem has been tackled using unsupervised . Bean and Riloff 1999 and supervised . Evans 2001 Ng and Cardie 2002a .

• Báo cáo khoa học
• Learning Noun Phrase Anaphoricity to Improve Coreference Resolution
• Issues in Representation and Optimization
• Vincent Ng
• 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