Abstract
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.- Anthology ID:
- 2022.lrec-1.24
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 227–236
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.24
- DOI:
- Cite (ACL):
- Pakawat Nakwijit and Matthew Purver. 2022. Misspelling Semantics in Thai. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 227–236, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Misspelling Semantics in Thai (Nakwijit & Purver, LREC 2022)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.lrec-1.24.pdf