# A Benchmark for Morphological Segmentation in Uyghur and Kazakh
## 2 Related Work
### Result Analysis
#### 4.6.3 Case Study  
To analyze the problems existing in different models further, this paper will provide cases to study the prediction results of the models. Figures 7 and 8 compare the ground truth and the predicted results. Combined with Table 4, several comparison models with higher experimental results are selected. We use bold font to mark the same morpheme as the ground truth.  
Figure 8: Case study of supervised models.  
Figure 6: Morphological error rates of different models on validation and test sets.  
Figure 7: Case study of unsupervised models.  
The experimental results in Figure 7 show that the morphemes are often segmented more finely when using unsupervised learning models compared with the ground truth. This problem is more obvious in MMSeg. For example, in the Uyghur word \"_i.e.g._\", three morphemes can be obtained after correct segmentation. Morfessor obtains two morphemes (one of which is correctly matched), and MMSeg obtains three morphemes (three of which are correctly matched). In the Kazakh word \"_komilpotentrepidi \"_, three morphemes can be obtained after correct segmentation, and Morfessor also obtains three morphemes (one of which is correctly matched). In comparison, MMSeg obtains six morphemes (one of which is correctly matched). This situation also occurs in the word \"_kakethile \"_. This is because, when training the model, this paper introduces the n-gram morpheme boundary assumption, so the results after model segmentation are all within the assumed boundary length. Compared with Uyghur, the length of a single morpheme in Kazakh is longer, so although MMSeg has improved in Kazakh, it is not very obvious. This also corresponds to why the model performs poorly in Kazakh, as shown in several analysis results mentioned above.  
The experimental results in Figure 8 show that the main errors in the models based on supervised learning appear in the stem. For example, in the word \"_i.e._, except for FEMSeg-CRF, which can correctly segment the stem, the results of other models are problematic. One point worth noting is that, because the dataset is manually annotated, inconsistent or inaccurate annotation results will be introduced, and the appearance of such problems will affect the model's performance. However, in Figure 8, it is found that although the correct segmentation result of the word \"_i.e._\" is incorrect, the results of other models are correct, except for the BiLSTM-CRF model. Furthermore, when encountering phonetic changes, both FEMSeg and FEMSeg-CRF models demonstrate better ability to correctly segment morphemes compared to other models. For example, in the word \"_i.e._\", after morphological segmentation, it is divided as \"_i.e._\", and after morphological restoration, it becomes \"_i.e._\".  
In summary, the methods proposed in this paper demonstrate superior performance in Uyghur and Kazakh stemming and morphological segmentation across various aspects. Both supervised and unsupervised models show strong capabilities in learning features. Thus, the FEMSeg and FEMSeg-CRF models do not rely on manual feature extraction processes. Instead, they leverage CNN, BiLSTM, and Transformer encoders to learn different features and the correlations between them. The MMSeg model also learns the correlations between n-grams and determines morphological boundaries through a unidirectional LSTM model and the masked attention mechanism. Both models are easy to implement and highly portable, providing valuable references for stemming and morphological segmentation in other agglutinative languages. Additionally, the reliability and stability of the models was demonstrated through 10-fold cross-validation.