Lexical Normalization for Code-switched Data and its Effect on POS Tagging

Rob van der Goot, Özlem Çetinoğlu

Publikation: Konference artikel i Proceeding eller bog/rapport kapitelKonferencebidrag i proceedingsForskningpeer review

Abstrakt

Lexical normalization, the translation of non-canonical data to standard language, has shown to improve the performance of many natural language processing tasks on social media. Yet, using multiple languages in one utterance, also called code-switching (CS), is frequently overlooked by these normalization systems, despite its common use in social media. In this paper, we propose three normalization models specifically designed to handle code-switched data which we evaluate for two language pairs: Indonesian-English and Turkish-German. For the latter, we introduce novel normalization layers and their corresponding language ID and POS tags for the dataset, and evaluate the downstream effect of normalization on POS tagging. Results show that our CS-tailored normalization models significantly outperform monolingual ones, and lead to 5.4\% relative performance increase for POS tagging as compared to unnormalized input.
OriginalsprogEngelsk
TitelProceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Antal sider13
ForlagAssociation for Computational Linguistics
Publikationsdatoapr. 2021
Sider2352-2365
StatusUdgivet - apr. 2021
BegivenhedEACL 2021 -
Varighed: 19 apr. 202123 apr. 2021

Konference

KonferenceEACL 2021
Periode19/04/202123/04/2021

Citationsformater