# The Effects of Data Size and Frequency Range on Distributional Semantic Models

arXiv: https://arxiv.org/abs/1609.08293 · HTML: https://mlsanity.com/paper/1609.08293/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2016.

## Built on by (most cited first, 6 of 6)

- [A comparative evaluation and analysis of three generations of Distributional Semantic Models](https://mlsanity.com/paper/2105.09825/) (2021)
- [Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation](https://mlsanity.com/paper/2104.03969/) (2021)
- [Efficient Multi-Modal Embeddings from Structured Data](https://mlsanity.com/paper/2110.02577/) (2021)
- [Quantifying Gender Bias in Consumer Culture](https://mlsanity.com/paper/2201.03173/) (2022)
- [Witgenstein's influence on artificial intelligence](https://mlsanity.com/paper/2302.01570/) (2023)
- [Mitigating Biases to Embrace Diversity: A Comprehensive Annotation Benchmark for Toxic Language](https://mlsanity.com/paper/2410.13313/) (2024)

## Abstract (arXiv)

This paper investigates the effects of data size and frequency range on distributional semantic models. We compare the performance of a number of representative models for several test settings over data of varying sizes, and over test items of various frequency. Our results show that neural network-based models underperform when the data is small, and that the most reliable model over data of varying sizes and frequency ranges is the inverted factorized model.