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Bidirectional LSTM Tagger for Latvian Grammatical Error Detection

  • Daiga DeksneEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11697)

Abstract

This paper reports on the development of a grammar error labeling system for the Latvian language. We choose to label six error types that are crucial for understanding a text as noted in a survey by native Latvian speakers. The error types are the following: an incorrect use of a preposition, an incorrect agreement in a phrase, an incorrect verb form, an incorrect noun form, an incorrect choice of the definite/indefinite ending of an adjective, and a missing comma. For neural network model training, a large amount of error-annotated training data is required. We generate artificial errors in a correct text to cope with the lack of manually annotated data. As a bidirectional Long Short-Term Memory neural network algorithm is considered the best for erroneous word detection by several authors, we chose this architecture. We train several models – models labeling a single type of error and models labeling all six types of errors. The precision for all types of errors reaches 94.61%, the recall – 94.08%.

Keywords

Grammar errors Neural network Word embeddings 

Notes

Acknowledgment

The research has been supported by the European Regional Development Fund within the project “Neural Network Modelling for Inflected Natural Languages” No. 1.1.1.1/16/A/215.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.TildeRigaLatvia

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