TAILIEUCHUNG - Báo cáo khoa học: "A Domain-Specific Statistical Surface Realizer"

We present a search-based approach to automatic surface realization given a corpus of domain sentences. Using heuristic search based on a statistical language model and a structure we introduce called an inheritance table we overgenerate a set of complete syntactic-semantic trees that are consistent with the given semantic structure and have high likelihood relative to the language model. These trees are then lexicalized, linearized, scored, and ranked. This model is being developed to generate real-time navigation instructions. . | A Domain-Specific Statistical Surface Realizer Jeffrey T. Russell Center for the Study of Language and Information Stanford University jefe@ Abstract We present a search-based approach to automatic surface realization given a corpus of domain sentences. Using heuristic search based on a statistical language model and a structure we introduce called an inheritance table we overgenerate a set of complete syntactic-semantic trees that are consistent with the given semantic structure and have high likelihood relative to the language model. These trees are then lexicalized linearized scored and ranked. This model is being developed to generate real-time navigation instructions. 1 Introduction The target application for this work is real-time interactive navigation instructions. Good directiongivers respond actively to a driver s actions and questions and express instructions relative to a large variety of landmarks times and distances. These traits require robust real-time natural language generation. This can be broken into three steps 1 generating a route plan 2 reasoning about the route and the user to produce an abstract representation of individual instructions and 3 realizing these instructions as sentences in natural language in our case English . We focus on the last of these steps given a structure that represents the semantic content of a sentence we want to produce an English sentence that expresses this content. According to the traditional division of content determination sentence planning and surface realization our work is primarily concerned with surface realization but also includes aspects of sentence planning. Our application requires robust flexibility within a restricted domain that is not well represented in the traditional corpora or tools. These requirements suggest using trainable stochastic generation. A number of statistical surface realizers have been described notably the FERGUS Bangalore and Rambow 2000 and HALogen systems .

• Báo cáo khoa học
• A Domain Specific
• Statistical Surface Realizer
• Jeffrey T
• Russell
• 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