# GliLem: Leveraging GliNER for Contextualized Lemmatization in Estonian

arXiv: https://arxiv.org/abs/2412.20597 · HTML: https://mlsanity.com/paper/2412.20597/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 29 Dec 2024.

## Builds on (most cited first, 15 of 15)

- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer](https://mlsanity.com/paper/2311.08526/) (2023)
- [BM25S: Orders of magnitude faster lexical search via eager sparse scoring](https://mlsanity.com/paper/2407.03618/) (2024)
- [Relevance-guided Supervision for OpenQA with ColBERT](https://mlsanity.com/paper/2007.00814/) (2020)
- [Applying the Transformer to Character-level Transduction](https://mlsanity.com/paper/2005.10213/) (2020)
- [Complementing Lexical Retrieval with Semantic Residual Embedding](https://mlsanity.com/paper/2004.13969/) (2020)
- [Universal Lemmatizer: A Sequence to Sequence Model for Lemmatizing Universal Dependencies Treebanks](https://mlsanity.com/paper/1902.00972/) (2019)
- [On the Role of Morphological Information for Contextual Lemmatization](https://mlsanity.com/paper/2302.00407/) (2023)
- [S~onajaht: Definition Embeddings and Semantic Search for Reverse Dictionary Creation](https://mlsanity.com/paper/2404.19430/) (2024)
- [Evaluating Shortest Edit Script Methods for Contextual Lemmatization](https://mlsanity.com/paper/2403.16968/) (2024)
- [Comparison of Current Approaches to Lemmatization: A Case Study in Estonian](https://mlsanity.com/paper/2404.15003/) (2024)

## Abstract (arXiv)

We present GliLem -- a novel hybrid lemmatization system for Estonian that enhances the highly accurate rule-based morphological analyzer Vabamorf with an external disambiguation module based on GliNER -- an open vocabulary NER model that is able to match text spans with text labels in natural language. We leverage the flexibility of a pre-trained GliNER model to improve the lemmatization accuracy of Vabamorf by 10% compared to its original disambiguation module and achieve an improvement over the token classification-based baseline. To measure the impact of improvements in lemmatization accuracy on the information retrieval downstream task, we first created an information retrieval dataset for Estonian by automatically translating the DBpedia-Entity dataset from English. We benchmark several token normalization approaches, including lemmatization, on the created dataset using the BM25 algorithm. We observe a substantial improvement in IR metrics when using lemmatization over simplistic stemming. The benefits of improving lemma disambiguation accuracy manifest in small but consistent improvement in the IR recall measure, especially in the setting of high k.