# Vector Space Morphology with Linear Discriminative Learning

arXiv: https://arxiv.org/abs/2107.03950 · HTML: https://mlsanity.com/paper/2107.03950/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 38 references that are in the map.
- First posted on arXiv 8 Jul 2021.

## Builds on (most cited first, 1 of 1)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)

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

- [Word-specific tonal realizations in Mandarin](https://mlsanity.com/paper/2405.07006/) (2024)
- [How trial-to-trial learning shapes mappings in the mental lexicon: Modelling Lexical Decision with Linear Discriminative Learning](https://mlsanity.com/paper/2207.00430/) (2022)
- [Frequency effects in Linear Discriminative Learning](https://mlsanity.com/paper/2306.11044/) (2023)
- [Is deeper always better? Replacing linear mappings with deep learning networks in the Discriminative Lexicon Model](https://mlsanity.com/paper/2410.04259/) (2024)
- [A Computational Model for the Assessment of Mutual Intelligibility Among Closely Related Languages](https://mlsanity.com/paper/2402.02915/) (2024)
- [An experimental and computational study of an Estonian single-person word naming](https://mlsanity.com/paper/2509.03143/) (2025)
- [Compound interpretation is based on analogy](https://mlsanity.com/paper/2610.01688/) (2026)

## Abstract (arXiv)

This paper presents three case studies of modeling aspects of lexical processing with Linear Discriminative Learning (LDL), the computational engine of the Discriminative Lexicon model (Baayen et al., 2019). With numeric representations of word forms and meanings, LDL learns to map one vector space onto the other, without being informed about any morphological structure or inflectional classes. The modeling results demonstrated that LDL not only performs well for understanding and producing morphologically complex words, but also generates quantitative measures that are predictive for human behavioral data. LDL models are straightforward to implement with the JudiLing package (Luo et al., 2021). Worked examples are provided for three modeling challenges: producing and understanding Korean verb inflection, predicting primed Dutch lexical decision latencies, and predicting the acoustic duration of Mandarin words.