TAILIEUCHUNG - Báo cáo khoa học: "Weakly Supervised Learning for Cross-document Person Name Disambiguation Supported by Information Extraction"

Among major categories of named entities (NEs, which in this paper refer to entity names, excluding the MUC time and numerical NEs), company and product names are often trademarked or uniquely registered, and hence less subject to name ambiguity. This paper focuses on cross-document disambiguation of person names. Previous research for cross-document name disambiguation applies vector space model (VSM) for context similarity, only using co-occurring words [Bagga & Baldwin 1998]. | Weakly Supervised Learning for Cross-document Person Name Disambiguation Supported by Information Extraction Cheng Niu Wei Li and Rohini K. Srihari Cymfony Inc. 600 Essjay Road Williamsville NY 14221 USA. cniu wei rohini @ Abstract It is fairly common that different people are associated with the same name. In tracking person entities in a large document pool it is important to determine whether multiple mentions of the same name across documents refer to the same entity or not. Previous approach to this problem involves measuring context similarity only based on co-occurring words. This paper presents a new algorithm using information extraction support in addition to co-occurring words. A learning scheme with minimal supervision is developed within the Bayesian framework. Maximum entropy modeling is then used to represent the probability distribution of context similarities based on heterogeneous features. Statistical annealing is applied to derive the final entity coreference chains by globally fitting the pairwise context similarities. Benchmarking shows that our new approach significantly outperforms the existing algorithm by 25 percentage points in overall F-measure. 1 Introduction Cross document name disambiguation is required for various tasks of knowledge discovery from textual documents such as entity tracking link discovery information fusion and event tracking. This task is part of the co-reference task if two mentions of the same name refer to same different entities by definition they should should not be co-referenced. As far as names are concerned co-reference consists of two sub-tasks i name disambiguation to handle the problem of different entities happening to use the same name ii alias association to handle the problem of the same entity using multiple names aliases . Message Understanding Conference MUC community has established within-document coreference standards MUC-7 1998 . Compared with within-document name disambiguation which

• Báo cáo khoa học
• Weakly Supervised Learning
• Cross document Person Name Disambiguation Supported
• Information Extraction
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Presupposition Relations between Verbs
• Galina Tremper
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• Multi Task Transfer Learning
• Weakly Supervised Relation Extraction
• Jing Jiang
• Hedge Classification
• Scientific Literature
• BMC Bioinformatics
• Biomedical text mining
• Natural language processing
• Database curation
• Machine learning
• 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
• 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