tailieunhanh - Báo cáo khoa học: "Applying Machine Learning to Chinese Temporal Relation Resolution"

Temporal relation resolution involves extraction of temporal information explicitly or implicitly embedded in a language. This information is often inferred from a variety of interactive grammatical and lexical cues, especially in Chinese. For this purpose, inter-clause relations (temporal or otherwise) in a multiple-clause sentence play an important role. In this paper, a computational model based on machine learning and heterogeneous collaborative bootstrapping is proposed for analyzing temporal relations in a Chinese multiple-clause sentence. The model makes use of the fact that events are represented in different temporal structures. . | Applying Machine Learning to Chinese Temporal Relation Resolution Wenjie Li Department of Computing The Hong Kong Polytechnic University Hong Kong cswjli@ Guihong Cao Department of Computing The Hong Kong Polytechnic University Hong Kong csghcao@ Abstract Temporal relation resolution involves extraction of temporal information explicitly or implicitly embedded in a language. This information is often inferred from a variety of interactive grammatical and lexical cues especially in Chinese. For this purpose inter-clause relations temporal or otherwise in a multiple-clause sentence play an important role. In this paper a computational model based on machine learning and heterogeneous collaborative bootstrapping is proposed for analyzing temporal relations in a Chinese multiple-clause sentence. The model makes use of the fact that events are represented in different temporal structures. It takes into account the effects of linguistic features such as tense aspect temporal connectives and discourse structures. A set of experiments has been conducted to investigate how linguistic features could affect temporal relation resolution. 1 Introduction In language studies temporal information describes changes and time of changes expressed in a language. Such information is critical in many typical natural language processing NLP applications . language generation and machine translation etc. Modeling temporal aspects of an event in a written text is more complex than capturing time in a physical time-stamped system. Event time may be specified explicitly in a sentence . ÍỀfl @ 1997 I Tit T W ffiR They solved the traffic problem of the city in 1997 or it may be left implicit to be recovered by readers from context. For example one may know that W W ÍỀfl @Tìẵ @W ffiR after the street bridge had been built they solved the traffic problem of the city yet without knowing the exact time when the street bridge was built. As reported by Partee

• Báo cáo khoa học
• Applying Machine Learning to Chinese
• Temporal Relation Resolution
• Wenjie Li
• 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