Creating Semantic Representations

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Abstract

In this chapter, we present the vector space model and some ways to further process such a representation: With feature hashing, random indexing, latent semantic analysis, non-negative matrix factorization, explicit semantic analysis and word embedding, a word or a text may be associated with a distributed semantic representation. Deep learning, explicit semantic networks and auxiliary non-linguistic information provide further means for creating distributed representations from linguistic data. We point to a few of the methods and datasets used to evaluate the many different algorithms that create a semantic representation, and we also point to some of the problems associated with distributed representations.

Original languageEnglish
Title of host publicationStatistical Semantics : Methods and Applications
PublisherSpringer
Publication date2020
Pages11-31
Chapter2
ISBN (Print)978-3-030-37249-1
ISBN (Electronic)978-3-030-37250-7
DOIs
Publication statusPublished - 2020

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