TAILIEUCHUNG - Báo cáo khoa học: "You’ve Got Answers: Towards Personalized Models for Predicting Success in Community Question Answering"

Question answering communities such as Yahoo! Answers have emerged as a popular alternative to general-purpose web search. By directly interacting with other participants, information seekers can obtain specific answers to their questions. However, user success in obtaining satisfactory answers varies greatly. | You ve Got Answers Towards Personalized Models for Predicting Success in Community Question Answering YandongLiu and Eugene Agichtein Emory University yliu49 eugene @ Abstract Question answering communities such as Yahoo Answers have emerged as a popular alternative to general-purpose web search. By directly interacting with other participants information seekers can obtain specific answers to their questions. However user success in obtaining satisfactory answers varies greatly. We hypothesize that satisfaction with the contributed answers is largely determined by the asker s prior experience expectations and personal preferences. Hence we begin to develop personalized models of asker satisfaction to predict whether a particular question author will be satisfied with the answers contributed by the community participants. We formalize this problem and explore a variety of content structure and interaction features for this task using standard machine learning techniques. Our experimental evaluation over thousands of real questions indicates that indeed it is beneficial to personalize satisfaction predictions when sufficient prior user history exists significantly improving accuracy over a one-size-fits-all prediction model. 1 Introduction Community Question Answering CQA has recently become a viable method for seeking information online. As an alternative to using generalpurpose web search engines information seekers now have an option to post their questions often complex specific and subjective on Community QA sites such as Yahoo Answers and have their questions answered by other users. Hundreds of millions of answers have already been posted for tens of millions of questions in Yahoo Answers. However the success of obtaining satisfactory answers in the available CQA portals varies greatly. In many cases the questions posted by askers go un-answered or are answered poorly never obtaining a satisfactory answer. In our recent work Liu et al. 2008 we

• Báo cáo khoa học
• Towards Personalized Models
• Predicting Success in Community Question Answering
• Yandong Liu and Eugene Agichtein Emory
• báo cáo khoa học
• báo cáo ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• BMC Cancer
• Oral squamous cell carcinoma
• Personalized precision medicine
• Systemic biomarker models
• Biomarker discovery
• 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
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• Lexical chain
• Phản hồi thông tin
• Bài toán PTCP
• Cấu trúc ngữ pháp
• Phân tích cú pháp xác suất
• CKY kết hợp xác suất
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• Hình thái học