topicmodels.etm
Topic Modelling in Embedding Spaces
Find topics in texts which are semantically embedded using techniques like word2vec or Glove. This topic modelling technique models each word with a categorical distribution whose natural parameter is the inner product between a word embedding and an embedding of its assigned topic. The techniques are explained in detail in the paper 'Topic Modeling in Embedding Spaces' by Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei (2019), available at doi:10.48550/arXiv.1907.04907.
- Version0.1.0
- R version≥ 2.10
- LicenseMIT
- Needs compilation?No
- Last release11/08/2021
Documentation
Team
Jan Wijffels
MaintainerShow author detailsBNOSAC
Show author detailsRolesCopyright holderAdji B. Dieng
Show author detailsRolesContributor, Copyright holderFrancisco J. R. Ruiz
Show author detailsRolesContributor, Copyright holderDavid M. Blei
Show author detailsRolesContributor, Copyright holder
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