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Spatiality and Its Semantic Consequence of the Quantitative Expression “一CCN” in Mandarin Chinese Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Tibetan Case Grammar Error Correction Method Based on Neural Networks

Authors : Cizhen Jiacuo, Secha Jia, Sangjie Duanzhu, Cairang Jia

Published in: Chinese Lexical Semantics

Publisher: Springer International Publishing

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Abstract

Grammar Error Correction (GEC) is an important researching subject among Nature Language Processing tasks. In this work, aiming at tackling with genitive and ergative grammatical errors in Tibetan formal text, we collect 1793563 consecutive sentence pairs as training set and 5000 sentence pairs with the same distribution as well as 1159 sentence pairs in different distributions as testing sets. In our approach, we firstly preprocess Tibetan text data with compositional rules and then build a neural network architecture which is a combination of BERT and Bi-LSTM, to estimate the probability of given token being genitive or ergative. In experiments, 98.38% and 86.16% in terms of accuracy are observed respectively in testing the proposed model on two different testing sets.

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previous chapter Statistical Analysis and Automatic Recognition of Grammatical Errors in Teaching Chinese as a Second Language
next chapter Chinese Text Error Correction Suggestion Generation Based on SoundShape Code
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Metadata
Title
Tibetan Case Grammar Error Correction Method Based on Neural Networks
Authors
Cizhen Jiacuo
Secha Jia
Sangjie Duanzhu
Cairang Jia
Copyright Year
2020
Book
Chinese Lexical Semantics

Print ISBN: 978-3-030-38188-2

Electronic ISBN: 978-3-030-38189-9

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-38189-9_43

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