Details

Autor(en) / Beteiligte

• Keya, Kamrun Naher
• Papanikolaou, Yannis
• Foulds, James R.

Titel

Neural Embedding Allocation: Distributed Representations of Topic Models

Ist Teil von

• Computational linguistics - Association for Computational Linguistics, 2022-12, Vol.48 (4), p.1021-1052

Ort / Verlag

One Broadway, 12th Floor, Cambridge, Massachusetts 02142, USA: MIT Press

Erscheinungsjahr

2022

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• We propose a method that uses neural embeddings to improve the performance of any given LDA-style topic model. Our method, called (NEA), deconstructs topic models (LDA or otherwise) into interpretable vector-space embeddings of words, topics, documents, authors, and so on, by learning neural embeddings to mimic the topic model. We demonstrate that NEA improves coherence scores of the original topic model by smoothing out the noisy topics when the number of topics is large. Furthermore, we show NEA’s effectiveness and generality in deconstructing and smoothing LDA, author-topic models, and the recent mixed membership skip-gram topic model and achieve better performance with the embeddings compared to several state-of-the-art models.