TAILIEUCHUNG - Báo cáo khoa học: "Using Lexical Dependency and Ontological Knowledge to Improve a Detailed Syntactic and Semantic Tagger of English"

This paper presents a detailed study of the integration of knowledge from both dependency parses and hierarchical word ontologies into a maximum-entropy-based tagging model that simultaneously labels words with both syntax and semantics. Our findings show that information from both these sources can lead to strong improvements in overall system accuracy: dependency knowledge improved performance over all classes of word, and knowledge of the position of a word in an ontological hierarchy increased accuracy for words not seen in the training data. . | Using Lexical Dependency and Ontological Knowledge to Improve a Detailed Syntactic and Semantic Tagger of English Andrew Finch NiCPATRt Kyoto Japan @ Ezra Black Epimenides Corp. New York UsA @ Young-Sook Hwang ETRI Seoul Korea yshwang7 @ Eiichiro Sumita NiCT-ATR Kyoto Japan @ Abstract This paper presents a detailed study of the integration of knowledge from both dependency parses and hierarchical word ontologies into a maximum-entropy-based tagging model that simultaneously labels words with both syntax and semantics. Our findings show that information from both these sources can lead to strong improvements in overall system accuracy dependency knowledge improved performance over all classes of word and knowledge of the position of a word in an ontological hierarchy increased accuracy for words not seen in the training data. The resulting tagger offers the highest reported tagging accuracy on this tagset to date. 1 Introduction Part-of-speech POS tagging has been one of the fundamental areas of research in natural language processing for many years. Most of the prior research has focussed on the task of labeling text with tags that reflect the words syntactic role in the sentence. In parallel to this the task of word sense disambiguation WSD the process of deciding in which semantic sense the word is being used has been actively researched. This paper addresses a combination of these two fields that is labeling running words with tags that comprise in addition to their syntactic function a broad semantic class that signifies the semantics of the word in the context of the sentence but does not necessarily provide information that is sufficiently finegrained as to disambiguate its sense. This differs National Institute of Information and Communications Technology ATR Spoken Language Communication Research Labs from what is commonly meant by WSD in that although each word may have many senses

• Báo cáo khoa học
• Using Lexical Dependency
• Ontological Knowledge to Improve a Detailed Syntactic
• Semantic Tagger of English
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Semi supervised Dependency Parsing
• Lexical Affinities
• Alexis Nasr
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• 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