# Feature2Vec: Distributional semantic modelling of human property knowledge

arXiv: https://arxiv.org/abs/1908.11439 · HTML: https://mlsanity.com/paper/1908.11439/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2019.

## Built on by (most cited first, 4 of 4)

- [VICE: Variational Interpretable Concept Embeddings](https://mlsanity.com/paper/2205.00756/) (2022)
- [A Method for Studying Semantic Construal in Grammatical Constructions with Interpretable Contextual Embedding Spaces](https://mlsanity.com/paper/2305.18598/) (2023)
- [Semantic features of object concepts generated with GPT-3](https://mlsanity.com/paper/2202.03753/) (2022)
- [Domain Embeddings for Generating Complex Descriptions of Concepts in Italian Language](https://mlsanity.com/paper/2402.16632/) (2024)

## Abstract (arXiv)

Feature norm datasets of human conceptual knowledge, collected in surveys of human volunteers, yield highly interpretable models of word meaning and play an important role in neurolinguistic research on semantic cognition. However, these datasets are limited in size due to practical obstacles associated with exhaustively listing properties for a large number of words. In contrast, the development of distributional modelling techniques and the availability of vast text corpora have allowed researchers to construct effective vector space models of word meaning over large lexicons. However, this comes at the cost of interpretable, human-like information about word meaning. We propose a method for mapping human property knowledge onto a distributional semantic space, which adapts the word2vec architecture to the task of modelling concept features. Our approach gives a measure of concept and feature affinity in a single semantic space, which makes for easy and efficient ranking of candidate human-derived semantic properties for arbitrary words. We compare our model with a previous approach, and show that it performs better on several evaluation tasks. Finally, we discuss how our method could be used to develop efficient sampling techniques to extend existing feature norm datasets in a reliable way.