Lexical Normalization
Lexical normalization is the task of translating/transforming a non standard text to a standard register.
Example:
new pix comming tomoroe
new pictures coming tomorrow
Datasets usually consists of tweets, since these naturally contain a fair amount of these phenomena.
For lexical normalization, only replacements on the word-level are annotated. Some corpora include annotation for 1-N and N-1 replacements. However, word insertion/deletion and reordering is not part of the task.
Leaderboards
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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Greatest papers with code
Adapting Sequence to Sequence models for Text Normalization in Social Media
Social media offer an abundant source of valuable raw data, however informal writing can quickly become a bottleneck for many natural language processing (NLP) tasks.
#3 best model for
Lexical Normalization
on LexNorm
MoNoise: Modeling Noise Using a Modular Normalization System
We show that MoNoise beats the state-of-the-art on different normalization benchmarks for English and Dutch, which all define the task of normalization slightly different.
SOTA for Lexical Normalization on LexNorm
A Multi-cascaded Deep Model for Bilingual SMS Classification
Our model achieves high accuracy for classification on this dataset and outperforms the previous model for multilingual text classification, highlighting language independence of McM.
LEXICAL NORMALIZATION MULTILINGUAL TEXT CLASSIFICATION TEXT CLASSIFICATION TRANSLITERATION