TAILIEUCHUNG - Báo cáo khoa học: "Solving Relational Similarity Problems Using the Web as a Corpus"

We present a simple linguistically-motivated method for characterizing the semantic relations that hold between two nouns. The approach leverages the vast size of the Web in order to build lexically-specific features. The main idea is to look for verbs, prepositions, and coordinating conjunctions that can help make explicit the hidden relations between the target nouns. Using these features in instance-based classifiers, we demonstrate state-of-the-art results on various relational similarity problems, including mapping noun-modifier pairs to abstract relations like TIME, LOCATION and CONTAINER, characterizing noun-noun compounds in terms of abstract linguistic predicates like CAUSE, USE, and FROM, classifying the relations. | Solving Relational Similarity Problems Using the Web as a Corpus Preslav Nakov EECS CS division University of California at Berkeley Berkeley CA 94720 USA nakov@ Marti A. Hearst School of Information University of California at Berkeley Berkeley CA 94720 USA hearst@ Abstract We present a simple linguistically-motivated method for characterizing the semantic relations that hold between two nouns. The approach leverages the vast size of the Web in order to build lexically-specific features. The main idea is to look for verbs prepositions and coordinating conjunctions that can help make explicit the hidden relations between the target nouns. Using these features in instance-based classifiers we demonstrate state-of-the-art results on various relational similarity problems including mapping noun-modifier pairs to abstract relations like TIME LOCATION and CONTAINER characterizing noun-noun compounds in terms of abstract linguistic predicates like CAUSE USE and FROM classifying the relations between nominals in context and solving SAT verbal analogy problems. In essence the approach puts together some existing ideas showing that they apply generally to various semantic tasks finding that verbs are especially useful features. 1 Introduction Despite the tremendous amount of work on word similarity see Budanitsky and Hirst 2006 for an overview there is surprisingly little research on the important related problem of relational similarity -semantic similarity between pairs of words. Students who took the SAT test before 2005 or who After January 2008 at the Linguistic Modeling Department Institute for Parallel Processing Bulgarian Academy of Sciences nakov@ are taking the GRE test nowadays are familiar with an instance of this problem - verbal analogy questions which ask whether . the relationship between ostrich and bird is more similar to that between lion and cat or rather between primate and monkey. These analogies are .

• Báo cáo khoa học
• Solving Relational Similarity Problems
• Web as a Corpus
• Preslav Nakov
• 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
• Natural Language Processing
• 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