Representing semantic graphs in a self-organizing map

Abstract

A long-standing problem in the field of connectionist language processing has been how to represent detailed linguistic structure. Approaches have ranged from the encoding of syntactic trees in Raam to the use of a mechanism to query meanings in a "gestalt layer". In this article, a technique called semantic self-organization is presented that allows for the optimal allocation and explicit representation of semantic dependency graphs on a Som-based grid. This technique has been successfully used in a connectionist natural language processing architecture called InSomNet to scale up the subsymbolic approach to represent sentences in the LinGO Redwoods HPSG Treebank drawn from the VerbMobil Project and annotated with rich semantic information. InSomNet was also shown to retain the cognitively plausible behavior detailed in psycholinguistics research. Consequently, semantic self-organization holds considerable promise as a basis for real-world natural language understanding systems that mimic human linguistic performance. © 2009 Springer-Verlag Berlin Heidelberg.