Linguists and philosophers since Aristotle have attempted to reduce natural language semantics in general, and the semantics of eventualities in particular, to a ‘language of mind’, expressed in terms of various collections of underlying language-independent primitive concepts. While such systems have proved insightful enough to suggest that such a universal conceptual representation is in some sense psychologically real, the primitive relations proposed, based on oppositions like agent-patient, event-state, etc., have remained incompletely convincing. This chapter proposes that the primitive concepts of the language of mind are ‘hidden’, or latent, and must be discovered automatically by detecting consistent patterns of entailment in the vast amounts of text that are made available by the internet using automatic syntactic parsers and machine learning to mine a form- and language-independent semantic representation language for natural language semantics. The representations involved combine a distributional representation of ambiguity with a language of logical form.
Form-Independent Meaning Representation for Eventualities
