TAILIEUCHUNG - Báo cáo khoa học: "Automatic Generation of Information-seeking Questions Using Concept Clusters"

One of the basic problems of efficiently generating information-seeking dialogue in interactive question answering is to find the topic of an information-seeking question with respect to the answer documents. In this paper we propose an approach to solving this problem using concept clusters. Our empirical results on TREC collections and our ambiguous question collection shows that this approach can be successfully employed to handle ambiguous and list questions. | Automatic Generation of Information-seeking Questions Using Concept Clusters Shuguang Li Department of Computer Science University of York YO10 5DD UK sgli@ Suresh Manandhar Department of Computer Science UniVersity of York YO10 5DD UK suresh@ Abstract One of the basic problems of efficiently generating information-seeking dialogue in interactive question answering is to find the topic of an information-seeking question with respect to the answer documents. In this paper we propose an approach to solving this problem using concept clusters. Our empirical results on TREC collections and our ambiguous question collection shows that this approach can be successfully employed to handle ambiguous and list questions. 1 Introduction Question Answering systems have received a lot of interest from NLP researchers during the past years. But it is often the case that traditional QA systems cannot satisfy the information needs of the users as the question processing part may fail to properly classify the question or the information needed for extracting and generating the answer is either implicit or not present in the question. In such cases interactive dialogue is needed to clarify the information needs and reformulate the question in a way that will help the system to find the correct answer. Due to the fact that casual users often ask questions with ambiguity and vagueness and most of the questions have multiple answers current QA systems return a list of answers for most questions. The answers for one question usually belong to different topics. In order to satisfy the information needs of the user information-seeking dialogue should take advantage of the inherent grouping of the answers. Several methods have been investigated for generating topics for questions in information-seeking dialogue. Hori et al. 2003 proposed a method for generating the topics for disambiguation questions. The scores are computed purely based on the syntactic ambiguity

• Báo cáo khoa học
• Automatic Generation of Information seeking Questions
• Concept Clusters
• Shuguang L
• 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