tailieunhanh - Báo cáo khoa học: "Question Answering as Question-Biased Term Extraction: A New Approach toward Multilingual QA"

This paper regards Question Answering (QA) as Question-Biased Term Extraction (QBTE). This new QBTE approach liberates QA systems from the heavy burden imposed by question types (or answer types). In conventional approaches, a QA system analyzes a given question and determines the question type, and then it selects answers from among answer candidates that match the question type. Consequently, the output of a QA system is restricted by the design of the question types. | Question Answering as Question-Biased Term Extraction A New Approach toward Multilingual QA Yutaka Sasaki Department of Natural Language Processing ATR Spoken Language Communication Research Laboratories 2-2-2 Hikaridai Seika-cho Soraku-gun Kyoto 619-0288 Japan Abstract This paper regards Question Answering QA as Question-Biased Term Extraction QBTE . This new QBTE approach liberates QA systems from the heavy burden imposed by question types or answer types . In conventional approaches a QA system analyzes a given question and determines the question type and then it selects answers from among answer candidates that match the question type. Consequently the output of a QA system is restricted by the design of the question types. The QBTE directly extracts answers as terms biased by the question. To confirm the feasibility of our QBTE approach we conducted experiments on the CRL QA Data based on 10-fold cross validation using Maximum Entropy Models MEMs as an ML technique. Experimental results showed that the trained system achieved in MRR and in Top5 accuracy. 1 Introduction The conventional Question Answering QA architecture is a cascade of the following building blocks Question Analyzer analyzes a question sentence and identifies the question types or answer types . Document Retriever retrieves documents related to the question from a large-scale document set. Answer Candidate Extractor extracts answer candidates that match the question types from the retrieved documents. Answer Selector ranks the answer candidates according to the syntactic and semantic conformity of each answer with the question and its context in the document. Typically question types consist of named entities . PERSON DATE and ORGANIZATION numerical expressions . LENGTH WEIGHT SPEED and class names . FLOWER BIRD and FOOD. The question type is also used for selecting answer candidates. For example if the question type of a given question is PERSON the .

• Báo cáo khoa học
• Question Answering as Question Biased Term Extraction
• A New Approach
• Multilingual QA
• 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