Semantic Network Language Generation based on a Semantic Networks Serialization Grammar

  • Authors:

  • Yintang Dai;Shiyong Zhang;Jidong Chen;Tianyuan Chen;Wei Zhang

  • Affiliations:

  • School of Computer Science and Technology, Fudan University, Shanghai, China 200433;School of Computer Science and Technology, Fudan University, Shanghai, China 200433;EMC Research China, Beijing, China 100084;School of Computer Science and Technology, Fudan University, Shanghai, China 200433;School of Computer Science and Technology, Fudan University, Shanghai, China 200433

  • Venue:
  • World Wide Web
  • Year:
  • 2010

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Abstract

This paper studies the Semantic Network Language Generation (SNLG), which is used to generate natural language from the information represented as Semantic Networks (SN). After a brief analysis of the challenges faced by SNLG, a Semantic Network Serialization Grammar (SNSG) is proposed to generate natural language from semantic networks. The SNSG is constituted by four components: (a) a semantic pattern approach to serializing a trivial semantic star into a language stream. (b) a transformative generation to serialize a trivial semantic tree by serializing semantic star recursively. (c) a procedure of trivialization to convert any complicated semantic star or semantic tree into composition of trivial semantic tree. (d) a mechanism of semantic pattern priority and semantic pattern network to guarantee a sentence generated from a semantic tree to be well formed. Based on the SNSG, a new approach of the content planning for SNLG is proposed to improve the content integrity. For discourse planning, a trivialization time splitting method is presented to make well-formed sentence, and a splitting time aggregation method is proposed to improve the readability of sentence. Finally a fully semantized Semantic Wiki system, the Natural Wiki, is developed to verify and demonstrate the theory and techniques addressed in this paper.