TAILIEUCHUNG - Báo cáo khoa học: "An Empirical Study of Active Learning with Support Vector Machines for Japanese Word Segmentation"

We explore how active learning with Support Vector Machines works well for a non-trivial task in natural language processing. We use Japanese word segmentation as a test case. In particular, we discuss how the size of a pool affects the learning curve. It is found that in the early stage of training with a larger pool, more labeled examples are required to achieve a given level of accuracy than those with a smaller pool. In addition, we propose a novel technique to use a large number of unlabeled examples effectively by adding them gradually to a pool. . | Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics ACL Philadelphia July 2002 pp. 505-512. An Empirical Study of Active Learning with Support Vector Machines for Japanese Word Segmentation Manabu Sassano Fujitsu Laboratories Ltd. 4-1-1 Kamikodanaka Nakahara-ku Kawasaki 211-8588 Japan sassano@ Abstract We explore how active learning with Support Vector Machines works well for a non-trivial task in natural language processing. We use Japanese word segmentation as a test case. In particular we discuss how the size of a pool affects the learning curve. It is found that in the early stage of training with a larger pool more labeled examples are required to achieve a given level of accuracy than those with a smaller pool. In addition we propose a novel technique to use a large number of unlabeled examples effectively by adding them gradually to a pool. The experimental results show that our technique requires less labeled examples than those with the technique in previous research. To achieve accuracy the proposed technique needs of labeled examples that are required when using the previous technique and only of labeled examples with random sampling. 1 Introduction Corpus-based supervised learning is now a standard approach to achieve high-performance in natural language processing. However the weakness of supervised learning approach is to need an annotated corpus the size of which is reasonably large. Even if we have a good supervised-learning method we cannot get high-performance without an annotated corpus. The problem is that corpus annotation is labour intensive and very expensive. In order to overcome this some unsupervised learning methods and minimally-supervised methods . Yarowsky 1995 Yarowsky and Wicentowski 2000 have been proposed. However such methods usually depend on tasks or domains and their performance often does not match one with a supervised learning method. Another promising .

• Báo cáo khoa học
• An Empirical Study of Active Learning
• Support Vector Machines
• Japanese Word Segmentation
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• A Combination of Active Learning
• Semi supervised Learning Starting
• Positive and Unlabeled Examples for Word Sense Disambiguation An Empirical Study on Japanese Web Search Query
• báo cáo ngôn ngữ
• 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