Building a semantic role labeling system for Vietnamese sentences Nguyễn Thanh Huy Trường Đại học Công nghệ Luận văn Thạc sĩ ngành: Khoa học máy tính; Mã số: 60 48 01 Người hướng dẫn: PhD Nguyễn Phương Thái Năm bảo vệ: 2011 Keywords: Nhận dạng ký tự; Tiếng Việt; Tin học Abtract: Function tags are labels which are defined as labels which contain abstract syntactic information of constituents such as noun phrases and prepositional phrase in a sentence Beside syntactic labels, function tags have significant roles in Nature Language Processing (NLP) Function tags are the key to answer several questions such as: who, whom, where, what, which often use in problems as: question answering, information extraction, and summarization These questions are very hard to answer if a NLP-system has only syntactic information In recent years, function tag labeling has been studied for languages such as English and Chinese In Vietnam, Nature Language Processing researches have recently studied in parsing syntactic problems In this thesis, I focus on building a function tag labeling system for Vietnamese With our experiment, I hope that our system will enrich data sources for NLP-research in Vietnamese later The structure of thesis includes three parts In the first part, I introduce function tags: their applications and why they attract researchers to explore the relationship between function tags with constituents in a sentence I also mention some modern research in function tag labeling problem including: related works, features, approaches and methods Besides, I will introduce Vietnamese function tags, corpus Viet Treebank, which is used in many NLP research in Vietnamese.In the second part, I represent function tag labeling approaches in recent research Briefly, for each approach I will propose and discuss aspects such as: approach strategy, features, methods, advantages and disadvantages For other methods the reader can refer to the bibliography In the last part, I will present our research in function tag labeling for Vietnamese I will discuss related works such as: processes to prepare data sources, selection features, proposal model and the effectiveness of our method Content