TAILIEUCHUNG - Báo cáo khoa học: "Abstraction and Generalisation in Semantic Role Labels: PropBank, VerbNet or both?"

Semantic role labels are the representation of the grammatically relevant aspects of a sentence meaning. Capturing the nature and the number of semantic roles in a sentence is therefore fundamental to correctly describing the interface between grammar and meaning. In this paper, we compare two annotation schemes, PropBank and VerbNet, in a task-independent, general way, analysing how well they fare in capturing the linguistic generalisations that are known to hold for semantic role labels, and consequently how well they grammaticalise aspects of meaning. We show that VerbNet is more verb-specific and better able to generalise to new semantic role. | Abstraction and Generalisation in Semantic Role Labels PropBank VerbNet or both Paola Merlo Linguistics Department University of Geneva 5 Rue de Candolle 1204 Geneva Switzerland Abstract Semantic role labels are the representation of the grammatically relevant aspects of a sentence meaning. Capturing the nature and the number of semantic roles in a sentence is therefore fundamental to correctly describing the interface between grammar and meaning. In this paper we compare two annotation schemes PropBank and VerbNet in a task-independent general way analysing how well they fare in capturing the linguistic generalisations that are known to hold for semantic role labels and consequently how well they grammaticalise aspects of meaning. We show that VerbNet is more verb-specific and better able to generalise to new semantic role instances while PropBank better captures some of the structural constraints among roles. We conclude that these two resources should be used together as they are complementary. 1 Introduction Most current approaches to language analysis assume that the structure of a sentence depends on the lexical semantics of the verb and of other predicates in the sentence. It is also assumed that only certain aspects of a sentence meaning are gram-maticalised. Semantic role labels are the representation of the grammatically relevant aspects of a sentence meaning. Capturing the nature and the number of semantic roles in a sentence is therefore fundamental to correctly describe the interface between grammar and meaning and it is of paramount importance for all natural language processing NLP applications that attempt to extract meaning representations from analysed text such as questionanswering systems or even machine translation. Lonneke Van Der Plas Linguistics Department University of Geneva 5 Rue de Candolle 1204 Geneva Switzerland The role of theories of semantic role lists is to obtain a set of semantic .

• Báo cáo khoa học
• Abstraction and Generalisation in Semantic Role Labels
• PropBank
• VerbNet
• 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