TAILIEUCHUNG - Báo cáo khoa học: "Towards Finding and Fixing Fragments: Using ML to Identify Non-Sentential Utterances and their Antecedents in Multi-Party Dialogue"

Non-sentential utterances (., shortanswers as in “Who came to the party?”— “Peter.”) are pervasive in dialogue. As with other forms of ellipsis, the elided material is typically present in the context (., the question that a short answer answers). We present a machine learning approach to the novel task of identifying fragments and their antecedents in multiparty dialogue. We compare the performance of several learning algorithms, using a mixture of structural and lexical features, and show that the task of identifying antecedents given a fragment can be learnt successfully (f () = .76); we discuss why the task of. | Towards Finding and Fixing Fragments Using ML to Identify Non-Sentential Utterances and their Antecedents in Multi-Party Dialogue David Schlangen Department of Linguistics University of Potsdam . Box 601553 D-14415 Potsdam Germany das@ Abstract Non-sentential utterances . shortanswers as in Who came to the party Peter. are pervasive in dialogue. As with other forms of ellipsis the elided material is typically present in the context . the question that a short answer answers . We present a machine learning approach to the novel task of identifying fragments and their antecedents in multiparty dialogue. We compare the performance of several learning algorithms using a mixture of structural and lexical features and show that the task of identifying antecedents given a fragment can be learnt successfully f .76 we discuss why the task of identifying fragments is harder f .41 and finally report on a combined task f .38 . 1 Introduction Non-sentential utterances NSUs as in 1 are pervasive in dialogue recent studies put the proportion of such utterances at around 10 across different types of dialogue Fernandez and Ginzburg 2002 Schlangen and Lascarides 2003 . 1 a. A Who came to the party B Peter. Peter came to the party. b. A I talked to Peter. B Peter Miller Was it Peter Miller you talked to c. A Who was this Peter Miller Was this Peter Miller Such utterances pose an obvious problem for natural language processing applications namely that the intended information in 1-a -B a proposition has to be recovered from the uttered information here an NP meaning with the help of information from the context. While some systems that automatically resolve such fragments have recently been developed Schlangen and Lascarides 2002 Fernandez et al. 2004a they have the drawback that they require deep linguistic processing full parses and also information about discourse structure and hence are not very robust. We have defined a well-defined subtask

• Báo cáo khoa học
• Towards Finding
• Fixing Fragments Using ML to Identify Non Sentential Utterances
• their Antecedents in Multi Party Dialogue
• 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