# Semantic Hilbert Space for Text Representation Learning

arXiv: https://arxiv.org/abs/1902.09802 · HTML: https://mlsanity.com/paper/1902.09802/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2019.

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

- [Quantum Cognitively Motivated Decision Fusion for Video Sentiment Analysis](https://mlsanity.com/paper/2101.04406/) (2021)
- [Adapting Pre-trained Language Models for Quantum Natural Language Processing](https://mlsanity.com/paper/2302.13812/) (2023)
- [A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models](https://mlsanity.com/paper/2306.03608/) (2023)
- [Syntax-Aware Complex-Valued Neural Machine Translation](https://mlsanity.com/paper/2307.08586/) (2023)
- [A quatum inspired neural network for geometric modeling](https://mlsanity.com/paper/2401.01801/) (2024)
- [Algebraic Quantum Intelligence: A New Framework for Reproducible Machine Creativity](https://mlsanity.com/paper/2602.14130/) (2026)

## Abstract (arXiv)

Capturing the meaning of sentences has long been a challenging task. Current models tend to apply linear combinations of word features to conduct semantic composition for bigger-granularity units e.g. phrases, sentences, and documents. However, the semantic linearity does not always hold in human language. For instance, the meaning of the phrase `ivory tower' can not be deduced by linearly combining the meanings of `ivory' and `tower'. To address this issue, we propose a new framework that models different levels of semantic units (e.g. sememe, word, sentence, and semantic abstraction) on a single textitSemantic Hilbert Space, which naturally admits a non-linear semantic composition by means of a complex-valued vector word representation. An end-to-end neural network~footnotehttps://github.com/wabyking/qnn is proposed to implement the framework in the text classification task, and evaluation results on six benchmarking text classification datasets demonstrate the effectiveness, robustness and self-explanation power of the proposed model. Furthermore, intuitive case studies are conducted to help end users to understand how the framework works.