# Language Modeling by Clustering with Word Embeddings for Text Readability Assessment

arXiv: https://arxiv.org/abs/1709.01888 · HTML: https://mlsanity.com/paper/1709.01888/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2017.

## Built on by (most cited first, 7 of 7)

- [Trends, Limitations and Open Challenges in Automatic Readability Assessment Research](https://mlsanity.com/paper/2105.00973/) (2021)
- [A Neural Pairwise Ranking Model for Readability Assessment](https://mlsanity.com/paper/2203.07450/) (2022)
- [Concept Identification of Directly and Indirectly Related Mentions Referring to Groups of Persons](https://mlsanity.com/paper/2107.00955/) (2021)
- [Word embeddings for topic modeling: an application to the estimation of the economic policy uncertainty index](https://mlsanity.com/paper/2111.00057/) (2021)
- [Strategies for Arabic Readability Modeling](https://mlsanity.com/paper/2407.03032/) (2024)
- [Cross-corpus Readability Compatibility Assessment for English Texts](https://mlsanity.com/paper/2306.09704/) (2023)
- [A study of Vietnamese readability assessing through semantic and statistical features](https://mlsanity.com/paper/2411.04756/) (2024)

## Abstract (arXiv)

We present a clustering-based language model using word embeddings for text readability prediction. Presumably, an Euclidean semantic space hypothesis holds true for word embeddings whose training is done by observing word co-occurrences. We argue that clustering with word embeddings in the metric space should yield feature representations in a higher semantic space appropriate for text regression. Also, by representing features in terms of histograms, our approach can naturally address documents of varying lengths. An empirical evaluation using the Common Core Standards corpus reveals that the features formed on our clustering-based language model significantly improve the previously known results for the same corpus in readability prediction. We also evaluate the task of sentence matching based on semantic relatedness using the Wiki-SimpleWiki corpus and find that our features lead to superior matching performance.