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date: 28 January 2021

Abstract and Keywords

Linguistic phenomena at all levels display properties of continua and show markedly gradient behavior. While categorical approaches have focused on the endpoints of distributions of linguistic phenomena, probabilistic linguistics focuses on the gradient middle ground. This chapter discusses how grammatical formalisms can be extended with a probabilistic interpretation, in particular within the data-oriented parsing framework. The chapter considers some well-known probabilistic models and show how these can deal with gradient phenomena in perception, production, acquisition and variation. Probabilistic linguistics proposes that frequencies are an inherent part of the human language system and that new expressions are constructed by generalizing over previously analyzed expressions. Relations with other models are discussed and the consequences of the probabilistic view for Universal Grammar.

Keywords: gradience, frequency, probability model, data-oriented parsing, constructions, grammaticality judgments, alternations, acquisition, productive units, universal grammar

Rens Bod

Rens Bod obtained his Ph.D. in Computational linguistics at the University of Amsterdam. He currently holds a chair in Artificial Intelligence and is affiliated with the University of St Andrews and at the University of Amsterdam. His main interests are in computational models of language acquisition and statistical natural language processing, but he has also worked in computational musicology, reasoning, and in the history of science and humanities. Rens published over 90 scholarly articles and is the author of Beyond Grammar: An Experience-Based Theory of Language (Stanford University, 1998). He also co-edited a number of handbooks, including Probabilistic Linguistics (MIT Press, 2003) and Data-Oriented Parsing (CSLI Publications/Chicago, 2003).

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