Learning Form-Meaning Mappings for Language

Proceedings of the AAAI Conference on Artificial Intelligence, 16

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 16

1999 SIGART/AAAI Doctoral Consortium

The proposed thesis research addresses two of the main obstacles to building agents that communicate using natural language: the need for richer representations of linguistic constructions that incorporate aspects of conceptual knowledge, context and goals; and the need for a principled approach to the automatic ac- quisition of such structures from examples. More generally, it explores the idea that patterns that arise in language are inextricably linked with and motivated by patterns of meaning and experience. This view, along with empirical evidence suggesting that linguistic knowledge at all levels can be characterized as mappings between form and meaning, serves as the basis for a computational model of the acquisition of simple phrasal and clausal constructions.