Lexical borrowing happens in almost all languages. To obtain more bilingual knowledge from monolingual corpora, we propose a neural network based loanword identification model for Uyghur. We build our model on a bidirectional LSTM - CNN framework, which can capture past and future information effectively and learn both word level and character level features from training data automatically. To overcome data sparsity that exists in model training, we also suggest three additional features , such as hybrid language model feature, pronunciation similarity feature and part-of-speech tagging feature to further improve the performance of our proposed approach. We conduct experiments on Chinese, Arabic and Russian loanword detection in Uyghur. Experimental results show that our proposed method outperforms several baseline models.
@InProceedings{MI18.291, author = {Chenggang Mi and Yating Yang and Lei Wang and Xi Zhou and Tonghai Jiang}, title = "{A Neural Network Based Model for Loanword Identification in Uyghur}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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