Despite rapid advances in multilingual natural language processing (NLP), the Bantu language Shona remains under-served in terms of morphological analysis and language-aware tools. This paper presents Shona spaCy, an open-source, rule-based morphological pipeline for Shona built on the spaCy framework. The system combines a curated JSON lexicon with linguistically grounded rules to model noun-class prefixes (Mupanda 1-18), verbal subject concords, tense-aspect markers, ideophones, and clitics, integrating these into token-level annotations for lemma, part-of-speech, and morphological features. The toolkit is available via pip install shona-spacy, with source code at https://github.com/HappymoreMasoka/shona-spacy and a PyPI release at https://pypi.org/project/shona-spacy/0.1.4/. Evaluation on formal and informal Shona corpora yields 90% POS-tagging accuracy and 88% morphological-feature accuracy, while maintaining transparency in its linguistic decisions. By bridging descriptive grammar and computational implementation, Shona spaCy advances NLP accessibility and digital inclusion for Shona speakers and provides a template for morphological analysis tools for other under-resourced Bantu languages.

Morphological defectivity is an intriguing and understudied phenomenon in linguistics. Addressing defectivity, where expected inflectional forms are absent, is essential for improving the accuracy of NLP tools in morphologically rich languages. However, traditional linguistic resources often lack coverage of morphological gaps as such knowledge requires significant human expertise and effort to document and verify. For scarce linguistic phenomena in under-explored languages, Wikipedia and Wiktionary often serve as among the few accessible resources. Despite their extensive reach, their reliability has been a subject of controversy. This study customizes a novel neural morphological analyzer to annotate Latin and Italian corpora. Using the massive annotated data, crowd-sourced lists of defective verbs compiled from Wiktionary are validated computationally. Our results indicate that while Wiktionary provides a highly reliable account of Italian morphological gaps, 7% of Latin lemmata listed as defective show strong corpus evidence of being non-defective. This discrepancy highlights potential limitations of crowd-sourced wikis as definitive sources of linguistic knowledge, particularly for less-studied phenomena and languages, despite their value as resources for rare linguistic features. By providing scalable tools and methods for quality assurance of crowd-sourced data, this work advances computational morphology and expands linguistic knowledge of defectivity in non-English, morphologically rich languages.

Computational morphology handles the language processing at the word level. It is one of the foundational tasks in the NLP pipeline for the development of higher level NLP applications. It mainly deals with the processing of words and word forms. Computational Morphology addresses various sub problems such as morpheme boundary detection, lemmatization, morphological feature tagging, morphological reinflection etc. In this paper, we present exhaustive survey of the methods for developing computational morphology related tools. We survey the literature in the chronological order starting from the conventional methods till the recent evolution of deep neural network based approaches. We also review the existing datasets available for this task across the languages. We discuss about the effectiveness of neural model compared with the traditional models and present some unique challenges associated with building the computational morphology tools. We conclude by discussing some recent and open research issues in this field.

How can large language models (LLMs) process and translate endangered languages? Many languages lack a large corpus to train a decent LLM; therefore existing LLMs rarely perform well in unseen, endangered languages. On the contrary, we observe that 2000 endangered languages, though without a large corpus, have a grammar book or a dictionary. We propose LINGOLLM, a training-free approach to enable an LLM to process unseen languages that hardly occur in its pre-training. Our key insight is to demonstrate linguistic knowledge of an unseen language in an LLM's prompt, including a dictionary, a grammar book, and morphologically analyzed input text. We implement LINGOLLM on top of two models, GPT-4 and Mixtral, and evaluate their performance on 5 tasks across 8 endangered or low-resource languages. Our results show that LINGOLLM elevates translation capability from GPT-4's 0 to 10.5 BLEU for 10 language directions. Our findings demonstrate the tremendous value of linguistic knowledge in the age of LLMs for endangered languages. Our data, code, and model generations can be found at https://github.com/LLiLab/llm4endangeredlang.

We introduce a morpheme-aware subword tokenization method that utilizes sub-character decomposition to address the challenges of applying Byte Pair Encoding (BPE) to Korean, a language characterized by its rich morphology and unique writing system. Our approach balances linguistic accuracy with computational efficiency in Pre-trained Language Models (PLMs). Our evaluations show that this technique achieves good performances overall, notably improving results in the syntactic task of NIKL-CoLA. This suggests that integrating morpheme type information can enhance language models' syntactic and semantic capabilities, indicating that adopting more linguistic insights can further improve performance beyond standard morphological analysis.

There are many complex and rich morphological subtleties in the Arabic language, which are very useful when analyzing traditional Arabic texts, especially in the historical and religious contexts, and help in understanding the meaning of the texts. Vocabulary separation means separating the word into different parts such as root and affix. In the morphological datasets, the variety of labels and the number of data samples helps to evaluate the morphological methods. In this paper, we present a benchmark data set for evaluating the methods of separating Arabic words which include about 223,690 words from the book of Sharia alIslam, which have been labeled by experts. In terms of the volume and variety of words, this dataset is superior to other existing data sets, and as far as we know, there are no Arabic Hadith Domain texts. To evaluate the dataset, we applied different methods such as Farasa, Camel, Madamira, and ALP to the dataset and we reported the annotation quality through four evaluation methods.

This paper investigates the effect of tokenizers on the downstream performance of pretrained language models (PLMs) in scriptio continua languages where no explicit spaces exist between words, using Japanese as a case study. The tokenizer for such languages often consists of a morphological analyzer and a subword tokenizer, requiring us to conduct a comprehensive study of all possible pairs. However, previous studies lack this comprehensiveness. We therefore train extensive sets of tokenizers, build a PLM using each, and measure the downstream performance on a wide range of tasks. Our results demonstrate that each downstream task has a different optimal morphological analyzer, and that it is better to use Byte-Pair-Encoding or Unigram rather than WordPiece as a subword tokenizer, regardless of the type of task.

We present Camelira, a web-based Arabic multi-dialect morphological disambiguation tool that covers four major variants of Arabic: Modern Standard Arabic, Egyptian, Gulf, and Levantine. Camelira offers a user-friendly web interface that allows researchers and language learners to explore various linguistic information, such as part-of-speech, morphological features, and lemmas. Our system also provides an option to automatically choose an appropriate dialect-specific disambiguator based on the prediction of a dialect identification component. Camelira is publicly accessible at http://camelira.camel-lab.com.

Lemmatization is one of the core concepts in natural language processing, thus creating a lemmatization tool is an important task. This paper discusses the construction of a lemmatization algorithm for the Uzbek language. The main purpose of the work is to remove affixes of words in the Uzbek language by means of the finite state machine and to identify a lemma (a word that can be found in the dictionary) of the word. The process of removing affixes uses a database of affixes and part of speech knowledge. This lemmatization consists of the general rules and a part of speech data of the Uzbek language, affixes, classification of affixes, removing affixes on the basis of the finite state machine for each class, as well as a definition of this word lemma.

Pre-trained language models such as BERT have been successful at tackling many natural language processing tasks. However, the unsupervised sub-word tokenization methods commonly used in these models (e.g., byte-pair encoding - BPE) are sub-optimal at handling morphologically rich languages. Even given a morphological analyzer, naive sequencing of morphemes into a standard BERT architecture is inefficient at capturing morphological compositionality and expressing word-relative syntactic regularities. We address these challenges by proposing a simple yet effective two-tier BERT architecture that leverages a morphological analyzer and explicitly represents morphological compositionality. Despite the success of BERT, most of its evaluations have been conducted on high-resource languages, obscuring its applicability on low-resource languages. We evaluate our proposed method on the low-resource morphologically rich Kinyarwanda language, naming the proposed model architecture KinyaBERT. A robust set of experimental results reveal that KinyaBERT outperforms solid baselines by 2% in F1 score on a named entity recognition task and by 4.3% in average score of a machine-translated GLUE benchmark. KinyaBERT fine-tuning has better convergence and achieves more robust results on multiple tasks even in the presence of translation noise.