TAILIEUCHUNG - Báo cáo khoa học: "Offline Strategies for Online Question Answering: Answering Questions Before They Are Asked"

Recent work in Question Answering has focused on web-based systems that extract answers using simple lexicosyntactic patterns. We present an alternative strategy in which patterns are used to extract highly precise relational information offline, creating a data repository that is used to efficiently answer questions. We evaluate our strategy on a challenging subset of questions, . “Who is ” questions, against a state of the art web-based Question Answering system. Results indicate that the extracted relations answer 25% more questions correctly and do so three orders of magnitude faster than the state of the art system. . | Offline Strategies for Online Question Answering Answering Questions Before They Are Asked Michael Fleischman Eduard Hovy Abdessamad Echihabi USC Information Sciences Institute 4676 Admiralty Way Marina del Rey CA 90292-6695 fleisch hovy echihabi @ Abstract Recent work in Question Answering has focused on web-based systems that extract answers using simple lexico-syntactic patterns. We present an alternative strategy in which patterns are used to extract highly precise relational information offline creating a data repository that is used to efficiently answer questions. We evaluate our strategy on a challenging subset of questions . Who is . questions against a state of the art web-based Question Answering system. Results indicate that the extracted relations answer 25 more questions correctly and do so three orders of magnitude faster than the state of the art system. 1 Introduction Many of the recent advances in Question Answering have followed from the insight that systems can benefit by exploiting the redundancy of information in large corpora. Brill et al. 2001 describe using the vast amount of data available on the World Wide Web to achieve impressive performance with relatively simple techniques. While the Web is a powerful resource its usefulness in Question Answering is not without limits. The Web while nearly infinite in content is not a complete repository of useful information. Most newspaper texts for example do not remain accessible on the Web for more than a few weeks. Further while Information Retrieval techniques are relatively successful at managing the vast quantity of text available on the Web the exactness required of Question Answering systems makes them too slow and impractical for ordinary users. In order to combat these inadequacies we propose a strategy in which information is extracted automatically from electronic texts offline and stored for quick and easy access. We borrow techniques from Text Mining in order to extract .

• Báo cáo khoa học
• Offline Strategies
• Online Question Answering
• Answering Questions Before They Are Asked
• 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