Machine Learning of Language from Distributional Evidence

Manning argues for acceptance of variable systems of language, and for searching for structure in these systems using probabilistic methods. Manning applies quantitative techniques to sentence structure, digging for the frequency, probability and likelihood that people will use specific turns of phrase in certain real-world contexts. Looking at distributions in the ways people express ideas in a language “can give a much richer description of how language is used.” Indeed, Manning finds that certain typical constraints on sentence structure in one language “show up as softer constraints and preferences in other languages.”



@FiorenzaMella