# Principal Components of the Meaning

arXiv: https://arxiv.org/abs/2009.08859 · HTML: https://mlsanity.com/paper/2009.08859/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2020.

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

- [Semantic Analysis for Automated Evaluation of the Potential Impact of Research Articles](https://mlsanity.com/paper/2104.12869/) (2021)
- [An Informational Space Based Semantic Analysis for Scientific Texts](https://mlsanity.com/paper/2205.15696/) (2022)

## Abstract (arXiv)

In this paper we argue that (lexical) meaning in science can be represented in a 13 dimension Meaning Space. This space is constructed using principal component analysis (singular decomposition) on the matrix of word category relative information gains, where the categories are those used by the Web of Science, and the words are taken from a reduced word set from texts in the Web of Science. We show that this reduced word set plausibly represents all texts in the corpus, so that the principal component analysis has some objective meaning with respect to the corpus. We argue that 13 dimensions is adequate to describe the meaning of scientific texts, and hypothesise about the qualitative meaning of the principal components.