A morpheme sequence and convolutional neural network based Kazakh text classification

Parhat, Sardar; Gao, Ting; Ablimit, Mijit; Hamdulla, Askar

doi:10.1109/apsipaasc47483.2019.9023280

Cited by 2 publications

(1 citation statement)

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“…It is extremely important to consider contextual information at the sentence level. Sentence-based reliable stemming can correctly predict stems and terms in noisy environments, providing an efficient approach for many aspects of multilingual natural language processing [17]. This paper proposes to use bidirectional LSTM, attention mechanism and conditional random field for sentence-level analysis by fusing character-level embedding and contextual information.…”

Section: Stemming In Derivative Languagesmentioning

confidence: 99%

A Character String-Based Stemming for Morphologically Derivative Languages

et al. 2022

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Morphologically derivative languages form words by fusing stems and suffixes, stems are important to be extracted in order to make cross lingual alignment and knowledge transfer. As there are phonetic harmony and disharmony when linguistic particles are combined, both phonetic and morphological changes need to be analyzed. This paper proposes a multilingual stemming method that learns morpho-phonetic changes automatically based on character based embedding and sequential modeling. Firstly, the character feature embedding at the sentence level is used as input, and the BiLSTM model is used to obtain the forward and reverse context sequence, and the attention mechanism is added to this model for weight learning, and the global feature information is extracted to capture the stem and affix boundaries; finally CRF model is used to learn more information from sequence features to describe context information more effectively. In order to verify the effectiveness of the above model, the model in this paper is compared with the traditional model on two different data sets of three derivative languages: Uyghur, Kazakh and Kirghiz. The experimental results show that the model in this paper has the best stemming effect on multilingual sentence-level datasets, which leads to more effective stemming. In addition, the proposed model outperforms other traditional models, and fully consider the data characteristics, and has certain advantages with less human intervention.

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Section: Stemming In Derivative Languagesmentioning

confidence: 99%