Semantic Network Language Generation based on a Semantic Networks Serialization Grammar |
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Authors: | Yintang Dai Shiyong Zhang Jidong Chen Tianyuan Chen Wei Zhang |
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Affiliation: | (1) Institute of Philosophy, Department of Logic, Academy of Sciences of the Czech Republic, Jlilska 1, 110 00 Praha 1, Czech Republic |
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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. |
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