Book chapter
Creating Semantic Representations
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.
Language: | English |
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Publisher: | Springer |
Year: | 2020 |
Pages: | 11-31 |
Journal subtitle: | Methods and Applications |
ISBN: | 3030372499 , 3030372502 , 9783030372491 and 9783030372507 |
Types: | Book chapter |
DOI: | 10.1007/978-3-030-37250-7_2 |
ORCIDs: | Nielsen, Finn Årup and Hansen, Lars Kai |