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Improving Text Models with Latent Feature Vector Representations | IEEE Conference Publication | IEEE Xplore

Improving Text Models with Latent Feature Vector Representations


Abstract:

Probabilistic topic models are widely used to discover potential topics in a collection of documents, while latent feature vector representations have been used to achiev...Show More

Abstract:

Probabilistic topic models are widely used to discover potential topics in a collection of documents, while latent feature vector representations have been used to achieve high performance in many NLP tasks. In this paper, we first make document topic vector representations by combining LDA and Topic2Vec, and then we perform document representations based on the topic vectors and the document vectors obtained through Doc2Vec training. Experimental results show that our new model has produced significant improvements in topic consistency and document classification tasks.
Date of Conference: 30 January 2019 - 01 February 2019
Date Added to IEEE Xplore: 14 March 2019
ISBN Information:
Print on Demand(PoD) ISSN: 2325-6516
Conference Location: Newport Beach, CA, USA

References

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