TAILIEUCHUNG - Báo cáo khoa học: "Japanese Dependency Parsing Using Co-occurrence Information and a Combination of Case Elements"

In this paper, we present a method that improves Japanese dependency parsing by using large-scale statistical information. It takes into account two kinds of information not considered in previous statistical (machine learning based) parsing methods: information about dependency relations among the case elements of a verb, and information about co-occurrence relations between a verb and its case element. | Japanese Dependency Parsing Using Co-occurrence Information and a Combination of Case Elements Takeshi Abekawa Graduate School of Education University of Tokyo abekawa@ Manabu Okumura Precision and Intelligence Laboratory Tokyo Institute of Technology oku@ Abstract In this paper we present a method that improves Japanese dependency parsing by using large-scale statistical information. It takes into account two kinds of information not considered in previous statistical machine learning based parsing methods information about dependency relations among the case elements of a verb and information about co-occurrence relations between a verb and its case element. This information can be collected from the results of automatic dependency parsing of large-scale corpora. The results of an experiment in which our method was used to rerank the results obtained using an existing machine learning based parsing method showed that our method can improve the accuracy of the results obtained using the existing method. 1 Introduction Dependency parsing is a basic technology for processing Japanese and has been the subject of much research. The Japanese dependency structure is usually represented by the relationship between phrasal units called bunsetsu each of which consists of one or more content words that may be followed by any number of function words. The dependency between two bunsetsus is direct from a dependent to its head. Manually written rules have usually been used to determine which bunsetsu another bunsetsu tends to modify but this method poses problems in terms of the coverage and consistency of the rules. The recent availability of larger-scale corpora annotated with dependency information has thus resulted in more work on statistical dependency analysis technologies that use machine learning algorithms Kudo and Matsumoto 2002 Sassano 2004 Uchimoto et al. 1999 Uchimoto et al. 2000 . Work on statistical Japanese dependency analysis has

• Báo cáo khoa học
• Japanese Dependency Parsing
• Co occurrence Information
• Combination of Case Elements
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Using Smaller Constituents Rather Than Sentences
• Active Learning
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• Dependency Parsing of Japanese
• Spoken Monologue
• Clause Boundaries
• A Unified Single Scan Algorithm
• Japanese Base Phrase Chunking
• Dependency Parsing
• Simultaneous English Japanese
• Spoken Language Translation Based on Incremental Dependency Parsing
• Transfer
• Sequential Labeling
• Semi spoken Language
• 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
• 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