The Interplay of Semantics and Morphology in Word Embeddings
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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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Apr 2017.
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Citing papers per year: 2021: 3; 2022: 2; 2023: 1.
6 papers.
- 2022Square One Bias in NLP: Towards a Multi-Dimensional Exploration of the Research Manifoldcited by 29
- 2023Explicit Morphological Knowledge Improves Pre-training of Language Models for Hebrewcited by 5
- 2021Integrating Approaches to Word Representationcited by 4
- 2022MorphTE: Injecting Morphology in Tensorized Embeddingscited by 3
- 2021Evaluation of Morphological Embeddings for the Russian Languagecited by 0
- 2021Evaluation of Morphological Embeddings for English and Russian Languagescited by 0
- 2023Explicit Morphological Knowledge Improves Pre-training of Language Models for Hebrew1 Nov 2023
- 2022MorphTE: Injecting Morphology in Tensorized Embeddings27 Oct 2022
- 2022Square One Bias in NLP: Towards a Multi-Dimensional Exploration of the Research Manifold20 Jun 2022
- 2021Integrating Approaches to Word Representation10 Sep 2021
- 2021Evaluation of Morphological Embeddings for English and Russian Languages11 Mar 2021
- 2021Evaluation of Morphological Embeddings for the Russian Language11 Mar 2021