On the other hand, if the computational goal is to demonstrate human-like performance in a biologically plausible (or biologically valid!) model of some form of language-related behavior, such as learning to apply words correctly to perceived objects or relationships, or learning to judge concept similarity, or to assess the tone (underlying sentiment) of a discourse segment, then symbolic representations need not play any role in the computational modeling. (However, to the extent that language is symbolic, and is a cognitive phenomenon, subsymbolic theories must ultimately explain how language can come about.) In the case of statistical semantics, practical applications such as question-answering based on large textual resources, retrieval of documents relevant to a query, or machine translation are at present greatly superior to logical systems that attempt to fully understand both the query or text they are confronted with and the knowledge they bring to bear on the task. But some of the trends pointed out above in trying to link subsymbolic and statistical representations with symbolic ones indicate that a gradual convergence of the various approaches to semantics is taking place. 