| Challenge: | False positive: a core NLP task of lemmatization seeks to map multiple forms of English verbs to a canonical one, known as the lemma. |
| Approach: | They propose a joint neural model for lemmatization and morphological tagging that achieves state-of-the-art results on 20 languages from the Universal Dependencies corpora. |
| Outcome: | The proposed model achieves state-of-the-art results on 20 languages from the Universal Dependencies corpora. |
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LemmaTag: Jointly Tagging and Lemmatizing for Morphologically Rich Languages with BRNNs (D18-1)
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| Challenge: | We compare morphologically rich languages with analytical languages like English due to the large vocabulary size and data sparsity. |
| Approach: | They propose a featureless neural network architecture that generates part-of-speech tags and lemmas for sentences by using bidirectional RNNs with character-level and word-level embeddings. |
| Outcome: | The proposed model outperforms state-of-the-art models in Czech, German, and Arabic. |
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. |
Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging (2020.acl-main)
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| Challenge: | a word can have multiple interpretations and is one of many inflected forms of the same concept or lemma. |
| Approach: | They propose to model morphological features jointly, whether lexicalized or non-lexicalised . their results are compared to Arabic and Egyptian Arabic . |
| Outcome: | The proposed model achieves 20% relative error reduction in Arabic and 11% in Egyptian Arabic. |
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. |
Improving Lemmatization of Non-Standard Languages with Joint Learning (N19-1)
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| Challenge: | Lemmatization is a task of mapping a token to its corresponding dictionary head-form to abstract away from orthographic and inflectional variation. |
| Approach: | They propose to improve lemmatization performance on non-standard historical languages . they propose an Encoder-Decoder architecture which enriches with sentence information . |
| Outcome: | The proposed model does not require POS or morphological annotations, which are not always available for historical corpora. |
Evaluating Shortest Edit Script Methods for Contextual Lemmatization (2024.lrec-main)
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| Challenge: | Modern contextual lemmatizers often rely on automatically induced Shortest Edit Scripts (SES) supervised contextual methods are used to perform lemma classification tasks. |
| Approach: | They propose to use masked language encoders to compute shortest edit Scripts (SES) SES is the number of edit operations to transform a word form into its lemma . |
| Outcome: | The proposed model outperforms language-specific models in all evaluation settings with seven languages of different morphological complexity. |
Lemmatisation & Morphological Analysis of Unedited Greek: Do Simple Tasks Need Complex Solutions? (2025.findings-acl)
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| Challenge: | Existing systems for part-of-speech tagging of unedited Greek text have outperformed traditional methods for morphological analysis and lemmatisation. |
| Approach: | They propose to combine nominal features into a single label and combine the three most distinctive features of verbs into another unified label. |
| Outcome: | The proposed models outperform traditional models in lemmatisation and morphological analysis and show that multi-task learning improves performance by transferring parameters. |
(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models’ Performance (2022.acl-short)
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| Challenge: | Inflection tasks have gained a lot of traction in recent years, mostly via SIGMORPHON's shared-tasks. |
| Approach: | They propose to use split-by-lemma to challenge the generalization capacity of morphological inflection models by employing harder train-test splits. |
| Outcome: | The proposed method is based on a split-by-lemma method that challenges the generalization capacity of the models. |
Neural Factor Graph Models for Cross-lingual Morphological Tagging (P18-1)
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| Challenge: | Existing approaches to morphological tagging are limited by the assumption that tag sets overlap . a limited amount of data is available for most languages to learn these morphology taggers. |
| Approach: | They propose a method for cross-lingual morphological tagging that relaxes this assumption . they use factorial conditional random fields with neural network potentials to smooth over superficial differences in the surface forms . |
| Outcome: | The proposed model can smooth over superficial differences in the surface forms and generate unseen or rare tag sets. |
Neural Disambiguation of Lemma and Part of Speech in Morphologically Rich Languages (2020.lrec-1)
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| Challenge: | a method for disambiguating the lemma and part of speech of ambiguous words is proposed . a morphological analyser produces multiple analyses for ambiguously words . |
| Approach: | They propose a method for disambiguating the lemma and part of speech of ambiguous words in context . they use a large un-annotated corpus of text and a morphological analyser to train neural networks on the output of the analyser . |
| Outcome: | The proposed method outperforms the state-of-the-art on POS and lemma disambiguation in morphologically rich languages using no manual disambiguations or data annotations. |