TAILIEUCHUNG - Báo cáo khoa học: "Ranking Algorithms for Named–Entity Extraction: Boosting and the Voted Perceptron"

This paper describes algorithms which rerank the top N hypotheses from a maximum-entropy tagger, the application being the recovery of named-entity boundaries in a corpus of web data. The first approach uses a boosting algorithm for ranking problems. The second approach uses the voted perceptron algorithm. Both algorithms give comparable, significant improvements over the maximum-entropy baseline. The voted perceptron algorithm can be considerably more efficient to train, at some cost in computation on test examples. isting statistical parser, giving significant improvements in parsing accuracy on Wall Street Journal data. Similar boosting algorithms have been applied to natural language generation,. | Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics ACL Philadelphia July 2002 pp. 489-496. Ranking Algorithms for Named-Entity Extraction Boosting and the Voted Perceptron Michael Collins AT T Labs-Research Florham Park New Jersey. mcollins@ Abstract This paper describes algorithms which rerank the top N hypotheses from a maximum-entropy tagger the application being the recovery of named-entity boundaries in a corpus of web data. The first approach uses a boosting algorithm for ranking problems. The second approach uses the voted perceptron algorithm. Both algorithms give comparable significant improvements over the maximum-entropy baseline. The voted perceptron algorithm can be considerably more efficient to train at some cost in computation on test examples. 1 Introduction Recent work in statistical approaches to parsing and tagging has begun to consider methods which incorporate global features of candidate structures. Examples of such techniques are Markov Random Fields Abney 1997 Della Pietra et al. 1997 Johnson et al. 1999 and boosting algorithms Freund et al. 1998 Collins 2000 Walker et al. 2001 . One appeal of these methods is their flexibility in incorporating features into a model essentially any features which might be useful in discriminating good from bad structures can be included. A second appeal of these methods is that their training criterion is often discriminative attempting to explicitly push the score or probability of the correct structure for each training sentence above the score of competing structures. This discriminative property is shared by the methods of Johnson et al. 1999 Collins 2000 and also the Conditional Random Field methods of Lafferty et al. 2001 . In a previous paper Collins 2000 a boosting algorithm was used to rerank the output from an ex isting statistical parser giving significant improvements in parsing accuracy on Wall Street Journal data. Similar boosting algorithms

• Báo cáo khoa học
• Ranking Algorithms
• Named–Entity Extraction
• Boosting and the Voted Perceptron
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Graph based Ranking Algorithms
• Sentence Extraction
• Applied to Text Summarization
• New Ranking Algorithms
• Parsing and Tagging Kernels over Discrete Structures
• the Voted Perceptron
• Feature selection
• Feature ranking methods
• Classification algorithms
• Classification accuracy
• Naive Bayes
• RBF network
• 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