TAILIEUCHUNG - Báo cáo khoa học: "Exploiting Non-local Features for Spoken Language Understanding"

In this paper, we exploit non-local features as an estimate of long-distance dependencies to improve performance on the statistical spoken language understanding (SLU) problem. The statistical natural language parsers trained on text perform unreliably to encode non-local information on spoken language. An alternative method we propose is to use trigger pairs that are automatically extracted by a feature induction algorithm. We describe a light version of the inducer in which a simple modification is efficient and successful. . | Exploiting Non-local Features for Spoken Language Understanding Minwoo Jeong and Gary Geunbae Lee Department of Computer Science Engineering Pohang University of Science and Technology San 31 Hyoja-dong Nam-gu Pohang 790-784 Korea stardust gblee @ Abstract In this paper we exploit non-local features as an estimate of long-distance dependencies to improve performance on the statistical spoken language understanding SLU problem. The statistical natural language parsers trained on text perform unreliably to encode non-local information on spoken language. An alternative method we propose is to use trigger pairs that are automatically extracted by a feature induction algorithm. We describe a light version of the inducer in which a simple modification is efficient and successful. We evaluate our method on an SLU task and show an error reduction of up to 27 over the base local model. 1 Introduction For most sequential labeling problems in natural language processing NLP a decision is made based on local information. However processing that relies on the Markovian assumption cannot represent higher-order dependencies. This longdistance dependency problem has been considered at length in computational linguistics. It is the key limitation in bettering sequential models in various natural language tasks. Thus we need new methods to import non-local information into sequential models. There are two types of method for using nonlocal information. One is to add edges to structure to allow higher-order dependencies and another is to add features or observable variables to encode the non-locality. An additional consistent edge of a linear-chain conditional random field CRF explicitly models the dependencies between distant occurrences of similar words Sutton and McCallum 2004 Finkel et al. 2005 . However this approach requires additional time complexity in inference learning time and it is only suitable for representing constraints by enforcing label consistency. We

• Báo cáo khoa học
• Exploiting Non local Features
• Spoken Language Understanding
• Minwoo Jeong and Gary Geunbae Lee
• 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