Hybrid lemmatization in HuSpaCy
Not on the map. Why?
Abstract (arXiv)
Lemmatization is still not a trivial task for morphologically rich languages. Previous studies showed that hybrid architectures usually work better for these languages and can yield great results. This paper presents a hybrid lemmatizer utilizing both a neural model, dictionaries and hand-crafted rules. We introduce a hybrid architecture along with empirical results on a widely used Hungarian dataset. The presented methods are published as three HuSpaCy models.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 30 references that are in the map. First posted on arXiv 13 Jun 2023.
Builds on
6 of 30 references.
References · 6
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
- 2016Enriching Word Vectors with Subword Informationcited by 1,583
- 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 723
- 2022HuSpaCy: an industrial-strength Hungarian natural language processing toolkitcited by 8
Built on by
Citing papers per year: 2024: 1.
1 paper.