Machine Learning for Natural Language Processing
Given at the ALTA summer school in Machine Learning for
Digital English Language Teaching
In this tutorial we will cover key machine learning concepts for natural language processing. In the first part we will discuss methods for classification and discuss how to use them for language-related tasks. We will begin with the perceptron algorithm for linear models and work our way towards models and features capturing higher level intuitions. In the second part we will look at the problem of assessing plausibility of a sentence, as it underpins many tasks in language assessment. We will look at n-gram language models and how they can be combined with elements of syntax, as well as some recent developments with neural netwrok architectures.
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