Text Error Correction (TEC) Algorithm

A Text Error Correction (TEC) Algorithm is a string error correction algorithm that can be implemented by a text error correction system to solve a text error correction task.

• Context:
  • It can range from being a Word/Token-level TEC Algorithm to being a Character-level TEC Algorithm.
  • It can (typically) be a TEC Model Training Algorithm.
• Example(s):
  • a Grammatical Error Correction Algorithm (for grammatical error correction tasks).
  • a Orthographic Error Correction Algorithm (for orthographic error correction tasks).
  • a Collocation Error Correction Algorithm (for collocation error correction tasks).
  • a Neural-based TEC Algorithm, an LM-based TEC Algorithm.
  • …
• Counter-Example(s):
  • a Text Error Detection Algorithm.
  • a Natural Language Generation Algorithm.
• See: DNA Error Correction Algorithm..

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References

2018b

• (Chollampatt & Ng, 2018) ⇒ Shamil Chollampatt, and Hwee Tou Ng. (2018). “A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction.” In: Proceedings of the Thirty-Second Conference on Artificial Intelligence (AAAI-2018).
  • QUOTE: We improve automatic automatic correction of grammatical, orthographic, and collocation errors in text using a multilayer convolutional encoder-decoder neural network. ...

2018a

• (Bryant & Briscoe, 2018) ⇒ Christopher Bryant, and Ted Briscoe. (2018). “Language Model Based Grammatical Error Correction Without Annotated Training Data.” In: Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications.
  • QUOTE: Since the end of the CoNLL-2014 shared task on grammatical error correction (GEC), research into language model (LM) based approaches to GEC has largely stagnated. In this paper, we re-examine LMs in GEC and show that it is entirely possible to build a simple system that not only requires minimal annotated data (~1000 sentences), but is also fairly competitive with several state-of-the-art systems. This approach should be of particular interest for languages where very little annotated training data exists, although we also hope to use it as a baseline to motivate future research.

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