TAILIEUCHUNG - Báo cáo khoa học: "Semantic Discourse Segmentation and Labeling for Route Instructions"

In order to build a simulated robot that accepts instructions in unconstrained natural language, a corpus of 427 route instructions was collected from human subjects in the office navigation domain. The instructions were segmented by the steps in the actual route and labeled with the action taken in each step. This flat formulation reduced the problem to an IE/Segmentation task, to which we applied Conditional Random Fields. We compared the performance of CRFs with a set of hand-written rules. The result showed that CRFs perform better with a success rate. . | Semantic Discourse Segmentation and Labeling for Route Instructions Nobuyuki Shimizu Department of Computer Science State University of New York at Albany Albany NY 12222 USA nobuyuki@shimi Abstract In order to build a simulated robot that accepts instructions in unconstrained natural language a corpus of 427 route instructions was collected from human subjects in the office navigation domain. The instructions were segmented by the steps in the actual route and labeled with the action taken in each step. This flat formulation reduced the problem to an IE Segmentation task to which we applied Conditional Random Fields. We compared the performance of CRFs with a set of hand-written rules. The result showed that CRFs perform better with a success rate. 1 Introduction To have seamless interactions with computers advances in task-oriented deep semantic understanding are of utmost importance. The examples include tutoring dialogue systems and the one described in this paper a natural language interface to mobile robots. Compared to more typical text processing tasks on newspapers for which we attempt shallow understandings and broad coverage for these domains vocabulary is limited and very strong domain knowledge is available. Despite this deeper understanding of unrestricted natural language instructions poses a real challenge due to the incredibly rich structures and creative expressions that people use. For example Just head straight through the hallway ignoring the rooms to the left and right of you but while going straight your going to eventually see a room facing you which is north enter it. Head straight. continue straight past the first three doors until you hit a corner. On that corner there are two doors one straight ahead of you and one on the right. Turn right and enter the room to the right and stop within. These utterances are taken from an office navigation corpus collected from undergrad volunteers at SUNY Albany. There is a good deal of .

• Báo cáo khoa học
• Semantic Discourse Segmentation
• Labeling for Route Instructions
• Nobuyuki Shimizu
• 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
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• Nghĩa từ vựng
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• Lexical chain
• Phản hồi thông tin
• Bài toán PTCP
• Cấu trúc ngữ pháp
• Phân tích cú pháp xác suất
• CKY kết hợp xác suất
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• Hình thái học