TAILIEUCHUNG - Báo cáo khoa học: "Advanced Online Learning for Natural Language Processing"

Introduction: Most research in machine learning has been focused on binary classification, in which the learned classifier outputs one of two possible answers. Important fundamental questions can be analyzed in terms of binary classification, but realworld natural language processing problems often involve richer output spaces. In this tutorial, we will focus on classifiers with a large number of possible outputs with interesting structure. Notable examples include information retrieval, part-of-speech tagging, NP chucking, parsing, entity extraction, and phoneme recognition. . | Advanced Online Learning for Natural Language Processing Koby Crammer Department of Computer and Information Science University of Pennsylvania Philadelphia PA 19104 crammer@ Introduction Most research in machine learning has been focused on binary classification in which the learned classifier outputs one of two possible answers. Important fundamental questions can be analyzed in terms of binary classification but real-world natural language processing problems often involve richer output spaces. In this tutorial we will focus on classifiers with a large number of possible outputs with interesting structure. Notable examples include information retrieval part-of-speech tagging NP chucking parsing entity extraction and phoneme recognition. Our algorithmic framework will be that of online learning for several reasons. First online algorithms are in general conceptually simple and easy to implement. In particular online algorithms process one example at a time and thus require little working memory. Second our example applications have all been treated successfully using online algorithms. Third the analysis of online algorithms uses simpler mathematical tools than other types of algorithms. Fourth the online learning framework provides a very general setting which can be applied to a broad setting of problems where the only machinery assumed is the ability to perform exact inference which computes a maxima over some score function. Goals 1 To provide the audience systematic methods to design analyze and implement efficiently learning algorithms for their specific complex-output problems from simple binary classification through multi-class categorization to information extraction parsing and speech recog nition. 2 To introduce new online algorithms which provide state-of-the-art performance in practice backed by interesting theoretical guarantees. Content The tutorial is divided into two parts. In the first half we introduce online learning and describe

• Báo cáo khoa học
• Advanced Online Learning
• Natural Language Processing
• Koby Crammer
• báo cáo khoa học
• báo cáo 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
• Ngôn ngữ lập trình
• Nhập nhằng cấu trúc
• Tri thức về ngôn ngữ
• Mô hình 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