TAILIEUCHUNG - Báo cáo khoa học: "Supersense Tagging of Unknown Nouns using Semantic Similarity"

The limited coverage of lexical-semantic resources is a significant problem for NLP systems which can be alleviated by automatically classifying the unknown words. Supersense tagging assigns unknown nouns one of 26 broad semantic categories used by lexicographers to organise their manual insertion into W ORD N ET. Ciaramita and Johnson (2003) present a tagger which uses synonym set glosses as annotated training examples. We describe an unsupervised approach, based on vector-space similarity, which does not require annotated examples but significantly outperforms their tagger. . | Supersense Tagging of Unknown Nouns using Semantic Similarity James R. Curran School of Information Technologies University of Sydney NSW 2006 Australia james@ Abstract The limited coverage of lexical-semantic resources is a significant problem for NLP systems which can be alleviated by automatically classifying the unknown words. Supersense tagging assigns unknown nouns one of 26 broad semantic categories used by lexicographers to organise their manual insertion into WordNet. Ciaramita and Johnson 2003 present a tagger which uses synonym set glosses as annotated training examples. We describe an unsupervised approach based on vector-space similarity which does not require annotated examples but significantly outperforms their tagger. We also demonstrate the use of an extremely large shallow-parsed corpus for calculating vector-space semantic similarity. 1 Introduction Lexical-semantic resources have been applied successful to a wide range of Natural Language Processing NLP problems ranging from collocation extraction Pearce 2001 and class-based smoothing Clark and Weir 2002 to text classification Baker and McCallum 1998 and question answering Pasca and Harabagiu 2001 . In particular WORDNET Fellbaum 1998 has significantly influenced research in NLP. Unfortunately these resource are extremely timeconsuming and labour-intensive to manually develop and maintain requiring considerable linguistic and domain expertise. Lexicographers cannot possibly keep pace with language evolution sense distinctions are continually made and merged words are coined or become obsolete and technical terms migrate into the vernacular. Technical domains such as medicine require separate treatment since common words often take on special meanings and a significant proportion of their vocabulary does not overlap with everyday vocabulary. Bur-gun and Bodenreider 2001 compared an alignment of WORDNET with the UMLS medical resource and found only a very small degree of overlap. .

• Báo cáo khoa học
• Supersense Tagging
• Unknown Nouns
• Semantic Similarity
• báo cáo khoa học
• mô hình 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
• 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