# Topic Model Supervised by Understanding Map

arXiv: https://arxiv.org/abs/2110.06043 · HTML: https://mlsanity.com/paper/2110.06043/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 17 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 1 of 1)

- [Understanding Graph and Understanding Map and their Potential Applications](https://mlsanity.com/paper/1711.06553/) (2017)

## Built on by (most cited first, 2 of 2)

- [Clustering with Neural Network and Index](https://mlsanity.com/paper/2212.03853/) (2022)
- [Min-Max-Jump distance and its applications](https://mlsanity.com/paper/2301.05994/) (2023)

## Abstract (arXiv)

Inspired by the notion of Center of Mass in physics, an extension called Semantic Center of Mass (SCOM) is proposed, and used to discover the abstract "topic" of a document. The notion is under a framework model called Understanding Map Supervised Topic Model (UM-S-TM). The devising aim of UM-S-TM is to let both the document content and a semantic network -- specifically, Understanding Map -- play a role, in interpreting the meaning of a document. Based on different justifications, three possible methods are devised to discover the SCOM of a document. Some experiments on artificial documents and Understanding Maps are conducted to test their outcomes. In addition, its ability of vectorization of documents and capturing sequential information are tested. We also compared UM-S-TM with probabilistic topic models like Latent Dirichlet Allocation (LDA) and probabilistic Latent Semantic Analysis (pLSA).