# A Benchmark for Morphological Segmentation in Uyghur and Kazakh
###### Abstract  
Morphological segmentation and stemming are foundational tasks in natural language processing. They have become effective ways to alleviate data sparsity in agglutinative languages because of the nature of agglutinative language word formation. Uyghur and Kazakh, as typical agglutinative languages, have made significant progress in morphological segmentation and stemming in recent years. However, the evaluation metrics used in previous work are character-level based, which may not comprehensively reflect the performance of models in morphological segmentation or stemming. Moreover, existing methods avoid manual feature extraction, but the model's ability to learn features is inadequate in complex scenarios, and the correlation between different features has not been considered. Consequently, these models lack representation in complex contexts, affecting their effective generalization in practical scenarios. To address these issues, this paper redefines the morphological-level evaluation metrics: F1-score and accuracy (ACC) for morphological segmentation and stemming tasks. In addition, two models are proposed for morpheme segmentation and stem extraction tasks: supervised model and unsupervised model. The supervised model learns character and contextual features simultaneously, then feature embeddings are input into a Transformer encoder to study the correlation between character and context embeddings. The last layer of the model uses a CRF or softmax layer to determine morphological boundaries. In unsupervised learning, an encoder-decoder structure introduces n-gram correlation assumptions and masked attention mechanisms, enhancing the correlation between characters within n-grams and reducing the impact of characters outside n-grams on boundaries. Finally, comprehensive comparative analyses of the performance of different models are conducted from various points of view. Experimental results demonstrate that: (1) The proposed evaluation method effectively reflects the differences in morphological segmentation and stemming for Uyghur and Kazakh; (2) Learning different features and their correlation can enhance the model's generalization ability in complex contexts. The proposed models achieve state-of-the-art performance on Uyghur and Kazakh datasets. agglutinativelanguages; morphological segmentation; stemming; feature extraction; evaluation metrics +
Footnote †: journal: _Sci_  
0000-0001-9999-4000-0001-9999-40001-9999-40001-9999-40001-9999-40001-9999-40001-9999-40001-99999-40001-99999-40001-99999-40001-99999-40001-99999-40001-99999-40001-999999-40001-999999-40001-999999-40001-999999-40 word, whereas the affix expresses grammatical information. Uyghur and Kazakh are typical agglutinative languages with extremely rich morphology [3] and a rich set of affixes expressing derivation or inflection. The lexical and grammatical structures of the two languages are very similar [4], and their word formation rules can be represented as [5]\(prefix_{1}+...+prefix_{n}+stem+suffix_{1}+...+suffix_{m}\). Stemming, as an extension of morphological segmentation, aims to extract the morphemes that represent the meaning of a word and remove the morphemes that express grammatical meanings [5]. Figure 1 shows examples for Uyghur and Kazakh of morphological segmentation and stemming. In Figure 1, the first line is words, and the second to fifth lines present the analysis results using the Leipzig Glossing Rules. The fifth line specifically marks the word's stem and the original form of morphemes before the phonological changes. From Figure 1, it can be observed that there are relations between morphological segmentation and stemming. In agglutinative languages, the first morpheme is the stem without prefixes. Furthermore, when the morphology of Uyghur and Kazakh is concatenated, phonological harmony may cause characters at the junction to occur: phenomena such as deletion, addition, and weakening [6]. Figure 1 shows three different phonological changes.