# Morphological Word Embeddings

arXiv: https://arxiv.org/abs/1907.02423 · HTML: https://mlsanity.com/paper/1907.02423/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2019.

## Built on by (most cited first, 8 of 8)

- [MorphTE: Injecting Morphology in Tensorized Embeddings](https://mlsanity.com/paper/2210.15379/) (2022)
- [Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density](https://mlsanity.com/paper/2111.01689/) (2021)
- [A Survey On Neural Word Embeddings](https://mlsanity.com/paper/2110.01804/) (2021)
- [Adverbs, Surprisingly](https://mlsanity.com/paper/2305.19650/) (2023)
- [Evaluation of Morphological Embeddings for the Russian Language](https://mlsanity.com/paper/2103.06628/) (2021)
- [Evaluation of Morphological Embeddings for English and Russian Languages](https://mlsanity.com/paper/2103.06884/) (2021)
- [Exploring the Potential of Feature Density in Estimating Machine Learning Classifier Performance with Application to Cyberbullying Detection](https://mlsanity.com/paper/2206.01949/) (2022)
- [Unsupervised Morphological Tree Tokenizer](https://mlsanity.com/paper/2406.15245/) (2024)

## Abstract (arXiv)

Linguistic similarity is multi-faceted. For instance, two words may be similar with respect to semantics, syntax, or morphology inter alia. Continuous word-embeddings have been shown to capture most of these shades of similarity to some degree. This work considers guiding word-embeddings with morphologically annotated data, a form of semi-supervised learning, encouraging the vectors to encode a word's morphology, i.e., words close in the embedded space share morphological features. We extend the log-bilinear model to this end and show that indeed our learned embeddings achieve this, using German as a case study.