# The Interplay of Semantics and Morphology in Word Embeddings

arXiv: https://arxiv.org/abs/1704.01938 · HTML: https://mlsanity.com/paper/1704.01938/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2017.

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

- [Square One Bias in NLP: Towards a Multi-Dimensional Exploration of the Research Manifold](https://mlsanity.com/paper/2206.09755/) (2022)
- [MorphTE: Injecting Morphology in Tensorized Embeddings](https://mlsanity.com/paper/2210.15379/) (2022)
- [Explicit Morphological Knowledge Improves Pre-training of Language Models for Hebrew](https://mlsanity.com/paper/2311.00658/) (2023)
- [Integrating Approaches to Word Representation](https://mlsanity.com/paper/2109.04876/) (2021)
- [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)

## Abstract (arXiv)

We explore the ability of word embeddings to capture both semantic and morphological similarity, as affected by the different types of linguistic properties (surface form, lemma, morphological tag) used to compose the representation of each word. We train several models, where each uses a different subset of these properties to compose its representations. By evaluating the models on semantic and morphological measures, we reveal some useful insights on the relationship between semantics and morphology.