TAILIEUCHUNG - Báo cáo khoa học: "Inducing Ontological Co-occurrence Vectors"

In this paper, we present an unsupervised methodology for propagating lexical cooccurrence vectors into an ontology such as WordNet. We evaluate the framework on the task of automatically attaching new concepts into the ontology. Experimental results show attachment accuracy in the first position and accuracy in the top-5 positions. This framework could potentially serve as a foundation for ontologizing lexical-semantic resources and assist the development of other largescale and internally consistent collections of semantic information. . | Inducing Ontological Co-occurrence Vectors Patrick Pantel Information Sciences Institute University of Southern California 4676 Admiralty Way Marina del Rey CA 90292 pantel@ Abstract In this paper we present an unsupervised methodology for propagating lexical cooccurrence vectors into an ontology such as WordNet. We evaluate the framework on the task of automatically attaching new concepts into the ontology. Experimental results show attachment accuracy in the first position and accuracy in the top-5 positions. This framework could potentially serve as a foundation for on-tologizing lexical-semantic resources and assist the development of other large-scale and internally consistent collections of semantic information. 1 Introduction Despite considerable effort there is still today no commonly accepted semantic corpus semantic framework notation or even agreement on precisely which aspects of semantics are most useful if at all . We believe that one important reason for this rather startling fact is the absence of truly wide-coverage semantic resources. Recognizing this some recent work on wide coverage term banks like WordNet Miller 1990 and CYC Lenat 1995 and annotated corpora like FrameNet Baker et al. 1998 Propbank Kingsbury et al. 2002 and Nombank Meyers et al. 2004 seeks to address the problem. But manual efforts such as these suffer from two drawbacks they are difficult to tailor to new domains and they have internal inconsistencies that can make automating the acquisition process difficult. In this work we introduce a general framework for inducing co-occurrence feature vectors for nodes in a WordNet-like ontology. We believe that this framework will be useful for a variety of applications including adding additional semantic information to existing semantic term banks by disambiguating lexical-semantic resources. Ontologizing semantic resources Recently researchers have applied text- and web-mining algorithms for automatically creating .

• Báo cáo khoa học
• Inducing Ontological
• Co occurrence Vectors
• Patrick Pantel
• 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