TAILIEUCHUNG - Báo cáo khoa học: "A Grammatical Approach to Understanding Textual Tables using Two-Dimensional SCFGs"

We present an elegant and extensible model that is capable of providing semantic interpretations for an unusually wide range of textual tables in documents. Unlike the few existing table analysis models, which largely rely on relatively ad hoc heuristics, our linguistically-oriented approach is systematic and grammar based, which allows our model (1) to be concise and yet (2) recognize a wider range of data models than others, and (3) disambiguate to a significantly finer extent the underlying semantic interpretation of the table in terms of data models drawn from relation database theory. . | A Grammatical Approach to Understanding Textual Tables using Two-Dimensional SCFGs Dekai Wu1 Ken Wing Kuen Lee Human Language Technology Center HKUST Department of Computer Science and Engineering University of Science and Technology Clear Water Bay Hong Kong dekai cswkl @ Abstract We present an elegant and extensible model that is capable of providing semantic interpretations for an unusually wide range of textual tables in documents. Unlike the few existing table analysis models which largely rely on relatively ad hoc heuristics our linguistically-oriented approach is systematic and grammar based which allows our model 1 to be concise and yet 2 recognize a wider range of data models than others and 3 disambiguate to a significantly finer extent the underlying semantic interpretation of the table in terms of data models drawn from relation database theory. To accomplish this the model introduces Viterbi parsing under two-dimensional stochastic CFGs. The cleaner grammatical approach facilitates not only greater coverage but also grammar extension and maintenance as well as a more direct and declarative link to semantic interpretation for which we also introduce a new cleaner data model. In disambiguation experiments on recognizing relevant data models of unseen web tables from different domains a blind evaluation of the model showed 60 precision and 80 recall. 1 Introduction Natural language processing has historically tended to emphasize understanding of linear strings sentences paragraphs discourse structure. The vast body of work that focuses on text understanding is often seen as an approximation of 1The authors would like to thank the Hong Kong Research Grants Council RGC for supporting this research in part through grants RGC6083 99E RGC6256 00E and DAG03 . spoken language understanding. Yet real-life text is actually heavily dependent on visual layout and formatting which compensate for cues normally found in spoken language but are absent in

• Báo cáo khoa học
• A Grammatical Approach to Understanding Textual Tables
• Two Dimensional SCFGs
• Long Papers
• 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