TAILIEUCHUNG - Báo cáo khoa học: "Brutus: A Semantic Role Labeling System Incorporating CCG, CFG, and Dependency Features"

We describe a semantic role labeling system that makes primary use of CCG-based features. Most previously developed systems are CFG-based and make extensive use of a treepath feature, which suffers from data sparsity due to its use of explicit tree configurations. CCG affords ways to augment treepathbased features to overcome these data sparsity issues. By adding features over CCG wordword dependencies and lexicalized verbal subcategorization frames (“supertags”), we can obtain an F-score that is substantially better than a previous CCG-based SRL system and competitive with the current state of the art. A manual error analysis reveals that parser errors. | Brutus A Semantic Role Labeling System Incorporating CCG CFG and Dependency Features Stephen A. Boxwell Dennis Mehay and Chris Brew Department of Linguistics The Ohio State University boxwe11 mehay cbrew @ Abstract We describe a semantic role labeling system that makes primary use of CCG-based features. Most previously developed systems are CFG-based and make extensive use of a treepath feature which suffers from data sparsity due to its use of explicit tree configurations. CCG affords ways to augment treepathbased features to overcome these data sparsity issues. By adding features over CCG wordword dependencies and lexicalized verbal subcategorization frames supertags we can obtain an F-score that is substantially better than a previous CCG-based SRL system and competitive with the current state of the art. A manual error analysis reveals that parser errors account for many of the errors of our system. This analysis also suggests that simultaneous incremental parsing and semantic role labeling may lead to performance gains in both tasks. 1 Introduction Semantic Role Labeling SRL is the process of assigning semantic roles to strings of words in a sentence according to their relationship to the semantic predicates expressed in the sentence. The task is difficult because the relationship between syntactic relations like subject and object do not always correspond to semantic relations like agent and patient . An effective semantic role labeling system must recognize the differences between different configurations a The man Arg0 opened the door Argl for him Arg3 today ArgM-TMP. b The door Arg 1 opened. c The door Arg 1 was opened by a man Arg0. We use Propbank Palmer et al. 2005 a corpus of newswire text annotated with verb predicate semantic role information that is widely used in the SRL literature Marquez et al. 2008 . Rather than describe semantic roles in terms of agent or patient Propbank defines semantic roles on a verb-by-verb basis. For .

• Báo cáo khoa học
• A Semantic Role Labeling System
• Incorporating CCG CFG
• Dependency Features
• 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