TAILIEUCHUNG - Báo cáo khoa học: "Learning Context-Dependent Mappings from Sentences to Logical Form"

We consider the problem of learning context-dependent mappings from sentences to logical form. The training examples are sequences of sentences annotated with lambda-calculus meaning representations. We develop an algorithm that maintains explicit, lambda-calculus representations of salient discourse entities and uses a context-dependent analysis pipeline to recover logical forms. The method uses a hidden-variable variant of the perception algorithm to learn a linear model used to select the best analysis. Experiments on context-dependent utterances from the ATIS corpus show that the method recovers fully correct logical forms with accuracy | Learning Context-Dependent Mappings from Sentences to Logical Form Luke S. Zettlemoyer and Michael Collins MIT CSAIL Cambridge MA 02139 lsz mcollins @ Abstract We consider the problem of learning context-dependent mappings from sentences to logical form. The training examples are sequences of sentences annotated with lambda-calculus meaning representations. We develop an algorithm that maintains explicit lambda-calculus representations of salient discourse entities and uses a context-dependent analysis pipeline to recover logical forms. The method uses a hidden-variable variant of the perception algorithm to learn a linear model used to select the best analysis. Experiments on context-dependent utterances from the ATIS corpus show that the method recovers fully correct logical forms with accuracy. 1 Introduction Recently researchers have developed algorithms that learn to map natural language sentences to representations of their underlying meaning He and Young 2006 Wong and Mooney 2007 Zettlemoyer and Collins 2005 . For instance a training example might be Sent. 1 List flights to Boston on Friday night. LF 1 x A to x bos A day x fri A during x night Here the logical form LF is a lambda-calculus expression defining a set of entities that are flights to Boston departing on Friday night. Most of this work has focused on analyzing sentences in isolation. In this paper we consider the problem of learning to interpret sentences whose underlying meanings can depend on the context in which they appear. For example consider an interaction where Sent. 1 is followed by the sentence Sent. 2 Show me the flights after 3pm. LF 2 x A to x bos Aday x fri A depart x 1500 In this case the fact that Sent. 2 describes flights to Boston on Friday must be determined based on the context established by the first sentence. We introduce a supervised hidden-variable approach for learning to interpret sentences in context. Each training example is a .

• Báo cáo khoa học
• Learning Context Dependent
• Mappings from Sentences to Logical Form
• Luke S
• Zettlemoyer and Michael Collins
• báo cáo khoa học
• báo cáo 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 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