Neural Embedding Allocation: Distributed Representations of Topic Models

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 embeddi...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Computational linguistics - Association for Computational Linguistics 2022-12, Vol.48 (4), p.1021-1052
Hauptverfasser: Keya, Kamrun Naher, Papanikolaou, Yannis, Foulds, James R.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
ISSN:0891-2017
1530-9312
DOI:10.1162/coli_a_00457