TAILIEUCHUNG - Báo cáo khoa học: "Improving Parsing and PP attachment Performance with Sense Information"

To date, parsers have made limited use of semantic information, but there is evidence to suggest that semantic features can enhance parse disambiguation. This paper shows that semantic classes help to obtain significant improvement in both parsing and PP attachment tasks. We devise a gold-standard sense- and parse tree-annotated dataset based on the intersection of the Penn Treebank and SemCor, and experiment with different approaches to both semantic representation and disambiguation. For the Bikel parser, we achieved a maximal error reduction rate over the baseline parser of and , for parsing and PP-attachment respectively, using an unsupervised WSD. | Improving Parsing and PP attachment Performance with Sense Information Eneko Agirre IXA NLP Group University of the Basque Country Donostia Basque Country Timothy Baldwin LT Group CSSE University of Melbourne Victoria 3010 Australia tim@ David Martinez LT Group CSSE University of Melbourne Victoria 3010 Australia davidm@ Abstract To date parsers have made limited use of semantic information but there is evidence to suggest that semantic features can enhance parse disambiguation. This paper shows that semantic classes help to obtain significant improvement in both parsing and PP attachment tasks. We devise a gold-standard sense- and parse tree-annotated dataset based on the intersection of the Penn Treebank and SemCor and experiment with different approaches to both semantic representation and disambiguation. For the Bikel parser we achieved a maximal error reduction rate over the baseline parser of and for parsing and PP-attachment respectively using an unsupervised WSD strategy. This demonstrates that word sense information can indeed enhance the performance of syntactic disambiguation. 1 Introduction Traditionally parse disambiguation has relied on structural features extracted from syntactic parse trees and made only limited use of semantic information. There is both empirical evidence and linguistic intuition to indicate that semantic features can enhance parse disambiguation performance however. For example a number of different parsers have been shown to benefit from lexicalisa-tion that is the conditioning of structural features on the lexical head of the given constituent Mager-man 1995 Collins 1996 Charniak 1997 Char-niak 2000 Collins 2003 . As an example of lexi-calisation we may observe in our training data that knife often occurs as the manner adjunct of open in prepositional phrases headed by with . open with a knife which would provide strong evidence for with a knife attaching to open

• Báo cáo khoa học
• Improving Parsing
• PP attachment Performance
• Sense Information
• báo cáo khoa học
• báo cáo ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Hierarchical Joint Learning
• Improving Joint Parsing and Named Entity Recognition
• Non Jointly Labeled Data
• ngôn ngữ tự nhiên
• Improving Arabic Dependency Parsing
• Form based
• Functional Morphological Features
• Improving Dependency Parsing
• Semantic Classes
• Eneko Agirre
• Improving data driven dependency parsing
• large scale LFG grammars
• Long Papers
• 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 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