Construction of the Uyghur Noun Morphological Re-Inflection Model Based on Hybrid Strategy

Abstract

In this paper, a hybrid strategy of rules and statistics is employed to implement the Uyghur Noun Re-inflection model. More specifically, completed Uyghur sentences are taken as an input, and these Uyghur sentences are marked with part of speech tagging, and the nouns in the sentences remain the form of the stem. In this model, relevant linguistic rules and statistical algorithms are used to find the most probable noun suffixes and output the Uyghur sentences after the nouns are re-inflected. With rules of linguistics artificially summed up, the training corpora are formed by the human–machine exchange. The final experimental result shows that the Uyghur morphological re-inflection model is of high performance and can be applied to various fields of natural language processing, such as Uyghur machine translation and natural language generation.

Constructing Uyghur Commonsense Knowledge Base by Knowledge Projection

“…In Uyghur, there are multivariant affixes with different variants of one affix added to harmonize the phonetic characteristics of the particular stem. For example, the plural affix has two variants " / " and they must be chosen based on the phonetic harmony rule between stem and variants [23]. Aizimaiti et al [24] proposed a rule-based variant-selection algorithm for Uyghur affixes based on Uyghur phonetic harmony.…”