Misspelling Semantics In Thai
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Abstract (arXiv)
User-generated content is full of misspellings. Rather than being just random noise, we hypothesise that many misspellings contain hidden semantics that can be leveraged for language understanding tasks. This paper presents a fine-grained annotated corpus of misspelling in Thai, together with an analysis of misspelling intention and its possible semantics to get a better understanding of the misspelling patterns observed in the corpus. In addition, we introduce two approaches to incorporate the semantics of misspelling: Misspelling Average Embedding (MAE) and Misspelling Semantic Tokens (MST). Experiments on a sentiment analysis task confirm our overall hypothesis: additional semantics from misspelling can boost the micro F1 score up to 0.4-2%, while blindly normalising misspelling is harmful and suboptimal.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 29 references that are in the map. First posted on arXiv 20 Jun 2022.
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References · 5
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2016Enriching Word Vectors with Subword Informationcited by 1,583
- 2018Learning Word Vectors for 157 Languagescited by 280
- 2020Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERTcited by 69
- 2021WangchanBERTa: Pretraining transformer-based Thai Language Modelscited by 26
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