Papers with Lemmatization
How low is too low? A monolingual take on lemmatisation in Indian languages (2021.naacl-main)
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| Challenge: | Prior work on ML based lemmatization focused on high resource languages, where data sets (word forms) are readily available. |
| Approach: | They propose to use neural methods to relate inflected forms of words to their dictionary form to reduce the sparse data problem. |
| Outcome: | The proposed methods can give competitive accuracy even in low resource setting. |
Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw Text (N19-1)
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| Challenge: | Using context-sensitive approaches to lemmatization can improve accuracy on unseen and unseense words. |
| Approach: | They propose to use inflection tables and Wikipedia sentences to train a lemmatizer with little or no labeled corpus data to combine type-based learning with context. |
| Outcome: | The proposed model generalizes from unambiguous examples, improving overall and especially on unseen words. |
Analysing cross-lingual transfer in lemmatisation for Indian languages (2020.coling-main)
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| Challenge: | Inference-based scripts such as Abjad are difficult for cross-lingual models to learn in extremely low resource scenarios. |
| Approach: | They evaluate cross-lingual approaches for low resource languages and compare their performance against other models using different linguistic factors. |
| Outcome: | The proposed model on six low resource languages from two different families is compared with monolingual models on morphologically rich Indian languages. |
Lemma Dilemma: On Lemma Generation Without Domain- or Language-Specific Training Data (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) can generate lemmas in context without prior fine-tuning. |
| Approach: | They compare in-context lemma generation with traditional fully supervised approaches . they use encoder-only supervised methods and cross-lingual methods . |
| Outcome: | The proposed model outperforms the traditional fully supervised approach in the context of lemmatization tasks. |
Lemmatization as a Classification Task: Results from Arabic across Multiple Genres (2025.emnlp-main)
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| Challenge: | Existing tools for lemmatization in morphologically rich languages with ambiguous orthography face inconsistent standards and limited genre coverage. |
| Approach: | They propose two new approaches that frame lemmatization as classification into a Lemma-POS-Gloss tagset, leveraging machine translation and semantic clustering. |
| Outcome: | The proposed models perform better than existing models and are more interpretable, the authors show. |