TAILIEUCHUNG - Báo cáo khoa học: "Identifying Agreement and Disagreement in Conversational Speech: Use of Bayesian Networks to Model Pragmatic Dependencies"

We describe a statistical approach for modeling agreements and disagreements in conversational interaction. Our approach first identifies adjacency pairs using maximum entropy ranking based on a set of lexical, durational, and structural features that look both forward and backward in the discourse. We then classify utterances as agreement or disagreement using these adjacency pairs and features that represent various pragmatic influences of previous agreement or disagreement on the current utterance. . | Identifying Agreement and Disagreement in Conversational Speech Use of Bayesian Networks to Model Pragmatic Dependencies Michel Galley Kathleen McKeown Julia Hirschberg and Elizabeth Shribergl Columbia University SRI International Computer Science Department Speech Technology and Research Laboratory 1214 Amsterdam Avenue 333 Ravenswood Avenue New York NY 10027 USA Menlo Park CA 94025 USA galley kathy julia @ ees@ Abstract We describe a statistical approach for modeling agreements and disagreements in conversational interaction. Our approach first identifies adjacency pairs using maximum entropy ranking based on a set of lexical durational and structural features that look both forward and backward in the discourse. We then classify utterances as agreement or disagreement using these adjacency pairs and features that represent various pragmatic influences of previous agreement or disagreement on the current utterance. Our approach achieves accuracy a increase over previous work. 1 Introduction One of the main features of meetings is the occurrence of agreement and disagreement among participants. Often meetings include long stretches of controversial discussion before some consensus decision is reached. Our ultimate goal is automated summarization of multi-participant meetings and we hypothesize that the ability to automatically identify agreement and disagreement between participants will help us in the summarization task. For example a summary might resemble minutes of meetings with major decisions reached consensus along with highlighted points of the pros and cons for each decision. In this paper we present a method to automatically classify utterances as agreement disagreement or neither. Previous work in automatic identification of agreement disagreement Hillard et al. 2003 demonstrates that this is a feasible task when various textual durational and acoustic features are available. We build on their approach and show that

• Báo cáo khoa học
• Identifying Agreement
• Disagreement in Conversational Speech
• Use of Bayesian Networks to Model Pragmatic Dependencies
• 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