基于双向修饰语的LSTM网络哈萨克语文本词性标注

2021 16th International Conference on Electronics Computer and Computation (ICECCO) Pub Date : 2021-11-25 DOI:10.1109/icecco53203.2021.9663794

Azamat Serek, A. Issabek, Adil Akhmetov, Alisher Sattarbek

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引用次数: 1

摘要

本文采用带双向修饰语的LSTM神经网络实现了哈萨克语文本词性标注。在自收集的哈萨克语句子数据集上构建了一个非常简单快速的标注器，并对其进行了测试和评估，准确率达到94%。基本上解决了训练样本数量少(大约100个哈萨克语句子)的标签问题。已经证明，在这种情况下可以达到相当高的精度。

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Part-of-speech tagging of Kazakh text via LSTM network with a bidirectional modifier

In this paper, part-of-speech tagging on Kazakh text has been implemented using an LSTM neural network with a bidirectional modifier. A quite simple and fast tagger has been built, tested and evaluated on the self-collected dataset of Kazakh sentences with an accuracy of 94 %. There was addressed basically the problem of tagging having a small number of training samples (around 100 Kazakh sentences). It has been shown that quite good accuracy can be achieved in this situation.

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2021 16th International Conference on Electronics Computer and Computation (ICECCO)

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