# Embedded Topic Models Enhanced by Wikification

arXiv: https://arxiv.org/abs/2410.02441 · HTML: https://mlsanity.com/paper/2410.02441/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 42 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Builds on (most cited first, 19 of 19)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence](https://mlsanity.com/paper/2107.02173/) (2021)
- [Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation](https://mlsanity.com/paper/1601.01343/) (2016)
- [Neural Models for Documents with Metadata](https://mlsanity.com/paper/1705.09296/) (2017)
- [The Dynamic Embedded Topic Model](https://mlsanity.com/paper/1907.05545/) (2019)
- [WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling](https://mlsanity.com/paper/1803.01328/) (2018)
- [Nonparametric Spherical Topic Modeling with Word Embeddings](https://mlsanity.com/paper/1604.00126/) (2016)
- [SpEL: Structured Prediction for Entity Linking](https://mlsanity.com/paper/2310.14684/) (2023)
- [Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC](https://mlsanity.com/paper/1706.01724/) (2017)
- [Corpus-level Fine-grained Entity Typing Using Contextual Information](https://mlsanity.com/paper/1606.07901/) (2016)
- [MetaLDA: a Topic Model that Efficiently Incorporates Meta information](https://mlsanity.com/paper/1709.06365/) (2017)
- [Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets](https://mlsanity.com/paper/1402.0480/) (2014)
- [Learning Multilingual Topics from Incomparable Corpus](https://mlsanity.com/paper/1806.04270/) (2018)
- [Crosslingual Topic Modeling with WikiPDA](https://mlsanity.com/paper/2009.11207/) (2020)
- [Dynamic and Static Topic Model for Analyzing Time-Series Document Collections](https://mlsanity.com/paper/1805.02203/) (2018)
- [What is Trending on Wikipedia? Capturing Trends and Language Biases Across Wikipedia Editions](https://mlsanity.com/paper/2002.06885/) (2020)
- [Entity Disambiguation via Fusion Entity Decoding](https://mlsanity.com/paper/2404.01626/) (2024)

## Abstract (arXiv)

Topic modeling analyzes a collection of documents to learn meaningful patterns of words. However, previous topic models consider only the spelling of words and do not take into consideration the homography of words. In this study, we incorporate the Wikipedia knowledge into a neural topic model to make it aware of named entities. We evaluate our method on two datasets, 1) news articles of textitNew York Times and 2) the AIDA-CoNLL dataset. Our experiments show that our method improves the performance of neural topic models in generalizability. Moreover, we analyze frequent terms in each topic and the temporal dependencies between topics to demonstrate that our entity-aware topic models can capture the time-series development of topics well.