标题： 上下文感知的土耳其语词形还原和形态标签方法 显示英文标题

标题： Context Aware Lemmatization and Morphological Tagging Method in Turkish

摘要： 一个定义单词的单词最小部分称为词根。 词根用于许多应用中以提高成功率，因为它们简化了单词。 在本研究中，介绍了词形还原模型（一种查找词根的方法）和形态标记模型（预测单词的语法知识）。 所提出的模型是为土耳其语开发的，并且两个模型在考虑单词含义的情况下进行预测。 在文献中，没有关于土耳其语词形还原的研究对单词含义敏感。 因此，本研究首次在文献中分享了土耳其语词形还原的模型和模型获得的结果。 在本研究中，在词形还原和形态标记模型中，使用双向LSTM进行单词拼写，使用土耳其BERT模型进行单词含义。 这些模型使用来自Universal Dependencies的IMST和PUD数据集进行训练。 模型训练的结果与SIGMORPHON 2019比赛的结果进行了比较。 比较结果表明我们的模型更优。 显示英文摘要

摘要： The smallest part of a word that defines the word is called a word root. Word roots are used to increase success in many applications since they simplify the word. In this study, the lemmatization model, which is a word root finding method, and the morphological tagging model, which predicts the grammatical knowledge of the word, are presented. The presented model was developed for Turkish, and both models make predictions by taking the meaning of the word into account. In the literature, there is no lemmatization study that is sensitive to word meaning in Turkish. For this reason, the present study shares the model and the results obtained from the model on Turkish lemmatization for the first time in the literature. In the present study, in the lemmatization and morphological tagging models, bidirectional LSTM is used for the spelling of words, and the Turkish BERT model is used for the meaning of words. The models are trained using the IMST and PUD datasets from Universal Dependencies. The results from the training of the models were compared with the results from the SIGMORPHON 2019 competition. The results of the comparisons revealed that our models were superior.