| Challenge: | Lemmatization is the process of finding the base form (lemma) of a word by considering its inflected forms. |
| Approach: | They propose a lemmatizer for Arabic with a dataset that can be used to test lemma accuracy. |
| Outcome: | The proposed algorithm outperforms state-of-the-art Arabic lemmatization in accuracy and speed. |
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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. |
Morphological Analysis and Disambiguation for Gulf Arabic: The Interplay between Resources and Methods (2020.lrec-1)
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| Challenge: | Despite advances in the field of natural language processing, many dialectal Arabic varieties are lagging behind . despite advances in NLP, many Arabic dialects are considered under-resourced . |
| Approach: | They propose a full morphological analysis and disambiguation system for Gulf Arabic . they use existing state-of-the-art morphology tools to investigate the effects of different data sizes and combinations of morphologists. |
| Outcome: | The proposed system improves on the existing system for Arabic . it is based on a set of data sizes and combinations of morphological analyzers . |
Arabic Natural Language Processing (2022.emnlp-tutorials)
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| Challenge: | This tutorial provides background information for system developers and researchers working with Arabic in its various forms. |
| Approach: | This tutorial provides the necessary background information for working with Arabic in its various forms. |
| Outcome: | This tutorial will explain various Arabic linguistic phenomena and review the state-of-the-art in Arabic processing. |
Utilizing Subword Entities in Character-Level Sequence-to-Sequence Lemmatization Models (2020.coling-main)
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| Challenge: | a novel character-level sequence-to-sequence lemmatization model uses generic n-gram embeddings to map word/lemma pairs . semitic languages, like Arabic and Hebrew, add other challenges to handle unseen words . |
| Approach: | They propose a character-level sequence-to-sequence lemmatization model . they use generic n-gram embeddings, concatenative (stems) and templatic (roots and patterns) morphological subwords. |
| Outcome: | The proposed model outperforms other linguistically-driven models with generic n-gram embeddings . the best system handles word/lemma pairs that are both unseen in the training data . |
Arabic Word-level Readability Visualization for Assisted Text Simplification (2022.emnlp-demos)
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| Challenge: | a Google Docs add-on for automatic Arabic word-level readability visualization is available for free. |
| Approach: | They propose a Google Docs add-on for automatic Arabic word-level readability visualization. |
| Outcome: | The proposed add-on can be used to assess the reading difficulty of a text and identify difficult words as part of manual text simplification. |
AGILe: The First Lemmatizer for Ancient Greek Inscriptions (2022.lrec-1)
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| Challenge: | Existing models for ancient Greek inscriptions are not performant on epigraphic data due to language differences . a lemmatizer for ancient inscription data can enable meaningful generalizations, we show . |
| Approach: | They propose to train an automatic lemmatizer for ancient Greek inscriptions with 80% accuracy . they also show that existing models are not performant on epigraphic data . |
| Outcome: | The proposed model achieves above 80% accuracy on epigraphic data, and makes it available to the community. |
A Large-Scale Leveled Readability Lexicon for Standard Arabic (2020.lrec-1)
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| Challenge: | a large-scale leveled readability lexicon for Modern Standard Arabic is not available in many other languages. |
| Approach: | They propose a large-scale leveled readability lexicon for Arabic with 26,000 lemmas . they manually annotate a lexico from three different regions in the arab world . |
| Outcome: | The proposed lexicon is publicly available for Arabic readability tasks. |
Advancing Arabic Diacritization: Improved Datasets, Benchmarking, and State-of-the-Art Models (2025.emnlp-main)
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| Challenge: | Arabic diacritics are typically omitted in written Arabic, leading to ambiguity . authors propose a methodology to analyze and refine a large diacritized corpus . |
| Approach: | They propose a methodology to analyze and refine a large diacritized corpus to improve training quality. |
| Outcome: | The proposed model achieves state-of-the-art results with 3.12% and 2.70% WER on WikiNews-2014 and Wikinews-2024. |
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. |
An Online Readability Leveled Arabic Thesaurus (2020.coling-demos)
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| Challenge: | a small minority of dictionaries specify the readability level of their words, let alone their lexical relations with other words. |
| Approach: | They propose to use Arabic lemmas, roots, English glosses, related Arabic words and phrases to provide a readability leveled Arabic thesaurus interface. |
| Outcome: | The proposed system provides the user with lemmas, roots, English glosses, related Arabic words and phrases, and readability on a five-level readability scale. |