TAILIEUCHUNG - Báo cáo khoa học: "Syntax is from Mars while Semantics from Venus! Insights from Spectral Analysis of Distributional Similarity Networks"

We study the global topology of the syntactic and semantic distributional similarity networks for English through the technique of spectral analysis. We observe that while the syntactic network has a hierarchical structure with strong communities and their mixtures, the semantic network has several tightly knit communities along with a large core without any such welldefined community structure. intriguing question, whereby we construct the syntactic and semantic distributional similarity network (DSN) and analyze their spectrum to understand their global topology. . | Syntax is from Mars while Semantics from Venus Insights from Spectral Analysis of Distributional Similarity Networks Chris Biemann Microsoft Powerset San Francisco Monojit Choudhury Microsoft Research Lab India monojitc@ Animesh Mukherjee Indian Institute of Technology Kharagpur India animeshm@ Abstract We study the global topology of the syntactic and semantic distributional similarity networks for English through the technique of spectral analysis. We observe that while the syntactic network has a hierarchical structure with strong communities and their mixtures the semantic network has several tightly knit communities along with a large core without any such well-defined community structure. 1 Introduction Syntax and semantics are two tightly coupled yet very different properties of any natural language - as if one is from Mars and the other from Venus . Indeed this exploratory work shows that the distributional properties of syntax are quite different from those of semantics. Distributional hypothesis states that the words that occur in the same contexts tend to have similar meanings Harris 1968 . Using this hypothesis one can define a vector space model for words where every word is a point in some n-dimensional space and the distance between them can be interpreted as the inverse of the semantic or syntactic similarity between their corresponding distributional patterns. Usually the co-occurrence patterns with respect to the function words are used to define the syntactic context whereas that with respect to the content words define the semantic context. An alternative but equally popular visualization of distributional similarity is through graphs or networks where each word is represented as nodes and weighted edges indicate the extent of distributional similarity between them. What are the commonalities and differences between the syntactic and semantic distributional patterns of the words of a .

• Báo cáo khoa học
• Syntax is from Mars
• Semantics from Venus! Insights
• Spectral Analysis
• Distributional Similarity Networks
• 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