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    <abstract>This paper presents three hybrid models that directly combine latent Dirichlet allocation and word embedding for distinguishing between speakers with and without Alzheimer’s disease from transcripts of picture descriptions. Two of our models get F-scores over the current state-of-the-art using automatic methods on the DementiaBank dataset.</abstract>
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%0 Conference Proceedings
%T Augmenting word2vec with latent Dirichlet allocation within a clinical application
%A Budhkar, Akshay
%A Rudzicz, Frank
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 jun
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F budhkar-rudzicz-2019-augmenting
%X This paper presents three hybrid models that directly combine latent Dirichlet allocation and word embedding for distinguishing between speakers with and without Alzheimer’s disease from transcripts of picture descriptions. Two of our models get F-scores over the current state-of-the-art using automatic methods on the DementiaBank dataset.
%R 10.18653/v1/N19-1414
%U https://aclanthology.org/N19-1414
%U https://doi.org/10.18653/v1/N19-1414
%P 4095-4099

Markdown (Informal)

[Augmenting word2vec with latent Dirichlet allocation within a clinical application](https://aclanthology.org/N19-1414) (Budhkar & Rudzicz, NAACL 2019)

• Augmenting word2vec with latent Dirichlet allocation within a clinical application (Budhkar & Rudzicz, NAACL 2019)

ACL

• Akshay Budhkar and Frank Rudzicz. 2019. Augmenting word2vec with latent Dirichlet allocation within a clinical application. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4095–4099, Minneapolis, Minnesota. Association for Computational Linguistics.