Morphological analysis, which includes analysis of part-of-speech (POS) tagging, stemming, and morpheme segmentation, is one of the key components in natural language processing (NLP), particularly for agglutinative languages. In this article, we investigate the morphological analysis of the Uyghur language, which is the native language of the people in the Xinjiang Uyghur autonomous region of western China. Morphological analysis of Uyghur is challenging primarily because of factors such as (1) ambiguities arising due to the likelihood of association of a multiple number of POS tags with a word stem or a multiple number of functional tags with a word suffix, (2) ambiguous morpheme boundaries, and (3) complex morphopholonogy of the language. Further, the unavailability of a manually annotated training set in the Uyghur language for the purpose of word segmentation makes Uyghur morphological analysis more difficult. In our proposed work, we address these challenges by undertaking a semisupervised approach of learning a Markov model with the help of a manually constructed dictionary of “suffix to tag” mappings in order to predict the most likely tag transitions in the Uyghur morpheme sequence. Due to the linguistic characteristics of Uyghur, we incorporate a prior belief in our model for favoring word segmentations with a lower number of morpheme units. Empirical evaluation of our proposed model shows an accuracy of about 82%. We further improve the effectiveness of the tag transition model with an active learning paradigm. In particular, we manually investigated a subset of words for which the model prediction ambiguity was within the top 20%. Manually incorporating rules to handle these erroneous cases resulted in an overall accuracy of 93.81%.

In this paper, we propose a transliteration approach based on semantic information (i.e., language origin and gender) which are automatically learnt from the person name, aiming to transliterate the person name of Uyghur into Chinese. The proposed approach integrates semantic scores (i.e., performance on language origin and gender detection) with general transliteration model and generates the semantic knowledge-based model which can produce the best candidate transliteration results. In the experiment, we use the datasets which contain the person names of different language origins: Uyghur and Chinese. The results show that the proposed semantic transliteration model substantially outperforms the general transliteration model and greatly improves the mean reciprocal rank (MRR) performance on two datasets, as well as aids in developing more efficient transliteration for named entities.

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Although named entity recognition achieved great success by introducing the neural networks, it is challenging to apply these models to low resource languages including Uyghur while it depends on a large amount of annotated training data. Constructing a well-annotated named entity corpus manually is very time-consuming and labor-intensive. Most existing methods based on the parallel corpus combined with the word alignment tools. However, word alignment methods introduce alignment errors inevitably. In this paper, we address this problem by a named entity tag transfer method based on the common neural machine translation. The proposed method marks the entity boundaries in Chinese sentence and translates the sentences to Uyghur by neural machine translation system, hope that neural machine translation will align the source and target entity by the self-attention mechanism. The experimental results show that the Uyghur named entity recognition system trained by the constructed corpus achieve good performance on the test set, with 73.80% F1 score(3.79% improvement by baseline).

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