TAILIEUCHUNG - Báo cáo khoa học: "Two Languages Are More Informative Than One *"

This paper presents a new approach for resolving lexical ambiguities in one language using statistical data on lexical relations in another language. This approach exploits the differences between mappings of words to senses in different languages. We concentrate on the problem of target word selection in machine translation, for which the approach is directly applicable, and employ a statistical model for the selection mechanism. The model was evaluated using two sets of Hebrew and German examples and was found to be very useful for disambiguation. . | Two Languages Are More Informative Than One Ido Dagan Computer Science Department Technion Haifa Israel and IBM Scientific Center Haifa Israel dagan@ Alon Itai Computer Science Department Technion Haifa Israel itai@ Ulrike Schwall IBM Scientific Center Institute for Knowledge Based Systems Heidelberg Germany schwall@dhdibml Abstract This paper presents a new approach for resolving lexical ambiguities in one language using statistical data on lexical relations in another language. This approach exploits the differences between mappings of words to senses in different languages. We concentrate on the problem of target word selection in machine translation for which the approach is directly applicable and employ a statistical model for the selection mechanism. The model was evaluated using two sets of Hebrew and German examples and was found to be very useful for disambiguation. 1 Introduction The resolution of lexical ambiguities in non-restricted text is one of the most difficult tasks of natural language processing. A related task in machine translation is target word selection - the task of deciding which target language word is the most appropriate equivalent of a source language word in context. In addition to the alternatives introduced from the different word senses of the source language word the target language may specify additional alternatives that differ mainly in their usages. Traditionally various linguistic levels were used to deal with this problem syntactic semantic and pragmatic. Computationally the syntactic methods are the easiest but are of no avail in the frequent situation when the different senses of the word show This research was partially supported by grant number 120-741 of the Israel Council for Research and Development the same syntactic behavior having the same part of speech and even the same subcategorization frame. Substantial application of semantic or pragmatic knowledge about the word and its .

• Báo cáo khoa học
• Two Languages
• More Informative Than One
• Ido Dagan
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• VNU Journal of Science
• Verbs of motion lexicalization patterns in English and Vietnamese
• English and Vietnamese
• A perspective from cognitive semantics
• Embody across two languages
• Data Structures and Algorithms
• Linked Lists includes Arrays
• Most programming languages
• Two kinds
• Singly Linked Lists
• Abstract Data Types
• 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