Word/Token-Level Text Error Correction (TEC) System
(Redirected from Word-Level TEC System)
Jump to navigation
Jump to search
A Word/Token-Level Text Error Correction (TEC) System is a text error correction system that implements a word-level TEC algorithm.
- Context:
- It can be a part of a Hybrid Character/Token-level TEC System.
- Example(s):
- Counter-Example(s):
- See: Grammatical Error Correction System.
References
2017
- https://github.com/atpaino/deep-text-corrector
- QUOTE: Deep Text Corrector uses TensorFlow to train sequence-to-sequence models that are capable of automatically correcting small grammatical errors in conversational written English (e.g. SMS messages). It does this by taking English text samples that are known to be mostly grammatically correct and randomly introducing a handful of small grammatical errors (e.g. removing articles) to each sentence to produce input-output pairs (where the output is the original sample), which are then used to train a sequence-to-sequence model.
While context-sensitive spell-check systems are able to automatically correct a large number of input errors in instant messaging, email, and SMS messages, they are unable to correct even simple grammatical errors. For example, the message "I'm going to store" would be unaffected by typical autocorrection systems, when the user most likely intendend to write "I'm going to the store". These kinds of simple grammatical mistakes are common in so-called "learner English", and constructing systems capable of detecting and correcting these mistakes has been the subect of multiple CoNLL shared tasks.
- QUOTE: Deep Text Corrector uses TensorFlow to train sequence-to-sequence models that are capable of automatically correcting small grammatical errors in conversational written English (e.g. SMS messages). It does this by taking English text samples that are known to be mostly grammatically correct and randomly introducing a handful of small grammatical errors (e.g. removing articles) to each sentence to produce input-output pairs (where the output is the original sample), which are then used to train a sequence-to-sequence model.