Abstract
This chapter discusses the theoretical and methodological principles of distributional semantic analysis. Over the last decade, Distributional Semantics has become very popular in Corpus Linguistics, building on very large corpora and extracting useful semantic information for numerous applications. In our “big data” era, Artificial Intelligence (AI) is carving its way into our daily life. AI’s algorithms in Natural Language Processing (NLP) learn from text collections with easily accessible information in order to find and predict knowledge patterns. This chapter explores the use of distributional analysis for terminological needs, i.e., in specialized domains. It focuses on what distributional analysis stands for, how it works, how it can be used for LSP and Terminology, and why it is useful for terminological needs.
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