Multilingual Stemming and Term extraction for Uyghur, Kazak and Kirghiz

Ablimit, Mijit; Parhat, Sardar; Hamdulla, Askar; Zheng, Thomas Fang

doi:10.23919/apsipa.2018.8659698

Cited by 2 publications

(1 citation statement)

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“…This experiment shows that ambiguity resolution can be effectively solved by making full use of context information, and the method surpasses several mainstream statistical methods in performance. M, Ablimit, et al [12] developed a sentence-level multilingual morphological processing tool. The tool provides sentence-level morpheme extraction, uses parallel corpora to train a statistical model, and achieves 98% accuracy in morpheme segmentation.…”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%

A Character String-Based Stemming for Morphologically Derivative Languages

et al. 2022

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Approaches, tools, algorithms, and methods for automatic term extraction: A systematic literature mapping

Andrade

Otálvaro

Jaramillo

et al. 2023

Preprint

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Automatic term extraction is a branch of Natural Language Processing (NLP) used to automatically generate lexicographic materials, such as glossaries, vocabularies, and dictionaries. It allows the creation of standard bases for building unified theories and translations between languages. Scientific literature shows great interest in the construction of automatic term extractors and includes several approaches, tools, algorithms, and methods that can be used for their construction; however, the number of articles in specialized databases is vast, and literature reviews are not recent. This paper presents a systematic literature mapping of the existing material for developing automatic term extractors to provide an overview of approaches, tools, algorithms, and methods used to create them. For this purpose, scientific articles in the domain published between 2015 and 2022 are reviewed and categorized. The mapping results show that among the most used approaches are statistical, with 21.85%; linguistic, with 9.75%; and hybrid, with 68.29%. In addition, there are various computational tools for terminology extraction where authors use different methods for their construction and whose results are measured under the criteria of precision and recall. Finally, 113 documents were selected to answer the research questions and to demonstrate how automatic term extractors are constructed. This paper presents a global summary of primary studies as an essential tool to approach this type of computational system construction.

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Multilingual Stemming and Term extraction for Uyghur, Kazak and Kirghiz

Cited by 2 publications

References 5 publications

A Character String-Based Stemming for Morphologically Derivative Languages

A Character String-Based Stemming for Morphologically Derivative Languages

Approaches, tools, algorithms, and methods for automatic term extraction: A systematic literature mapping

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