TAILIEUCHUNG - Báo cáo khoa học: "On2L - A Framework for Incremental Ontology Learning in Spoken Dialog Systems"

An open-domain spoken dialog system has to deal with the challenge of lacking lexical as well as conceptual knowledge. As the real world is constantly changing, it is not possible to store all necessary knowledge beforehand. Therefore, this knowledge has to be acquired during the run time of the system, with the help of the out-of-vocabulary information of a speech recognizer. As every word can have various meanings depending on the context in which it is uttered, additional context information is taken into account, when searching for the meaning of such a word. In this paper, I will present. | On2L - A Framework for Incremental Ontology Learning in Spoken Dialog Systems Berenike Loos European Media Laboratory GmbH Schloss-Wolfsbrunnenweg 33 69118 Heidelberg Germany Abstract An open-domain spoken dialog system has to deal with the challenge of lacking lexical as well as conceptual knowledge. As the real world is constantly changing it is not possible to store all necessary knowledge beforehand. Therefore this knowledge has to be acquired during the run time of the system with the help of the out-of-vocabulary information of a speech recognizer. As every word can have various meanings depending on the context in which it is uttered additional context information is taken into account when searching for the meaning of such a word. In this paper I will present the incremental ontology learning framework On2L. The defined tasks for the framework are the hypernym extraction from Internet texts for unknown terms delivered by the speech recognizer the mapping of those and their hypernyms into ontological concepts and instances and the following integration of them into the system s ontology. 1 Introduction A computer system which has to understand and generate natural language needs knowledge about the real world. As the manual modeling and maintenance of those knowledge structures . ontologies are both time and cost consuming there exists a demand to build and populate them automatically or at least semi automatically. This is possible by analyzing unstructured semi-structured or fully structured data by various linguistic as well as statistical means and by converting the results into an ontological form. In an open-domain spoken dialog system the automatic learning of ontological concepts and corresponding relations between them is essential as a complete manual modeling of them is neither practicable nor feasible as the real world and its objects models and processes are constantly changing and so are their denotations.

• Báo cáo khoa học
• On2L A Framework
• Incremental Ontology
• Learning in Spoken Dialog Systems
• 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