Conceptual Copy Editing
Copy-Editing support tools are in demand these days due to the rise in content creation job all over the world. So, rapid copy-editing and grammar check tools are in great demand these days in the market. By injecting machine intelligence into this process, we propose to create an autonomous copy-editing suite that could enhance the lives of millions of writers and their respective e-publisher companies.
Our clients are three major Fortune 100 listed e-publishers with more than 100+ works published per month, they needed a tool that could streamline their copy-editing process. Pushing error correction rates to the maximum, they hope to achieve consistent workflow by identifying the papers and articles and finding which of them need editing and corrections. This is done by a versatile scoring system that can easily identify the rate of correction that has to be done and checks the overall quality of the text. The context of the text is maintained thoroughly adhering to the specifications provided by the clients. Utilizing this model, the clients can streamline most of the manual checking that has to be done before publication and also get a more error-free auto grammar correction module which can boost productivity highly.
There are certain challenges pertaining to various modules in this tool, they are listed below:
Overfitting- There is a major chance of the textual data overfitting to the specifications as defined by the clients which can ruin the process of contextual extraction proposed by our model.
Vocabulary Diversity - The variety of language authors use can sometimes affect the grammatical construction of the text, creating a major challenge for our model.
Annotation Modes - When the text is annotated after scoring for the process of correction, it has to be clear and concise at pointing out the errors and should be as expansive as possible.
- The documents are ordered and organized to the specifications of the clients.
Texts are then run through various data organization and analysis models which help us to create the score that is important for selecting the articles according to their priority of correction.
A compare and contrast operation is done between the created score and the error sentences.
The document is then corrected to specification.
By tackling the aforementioned challenges, we have created a set of solutions that will rectify these problem headfirst:
The author’s manuscript is pre-processed, the operations of textual tokenization, lemmatization, stop words demarcations are run which extract the vital information needed for the model’s functioning.
The document metadata, format templates, and content specification as provided by the clients are also utilized to streamline this step.
The text is then regularized, i.e., the outliers regarding the text data are identified and used to rectify further errors.
A scorecard is then created which can be compared with the identified error patterns and is used to compartmentalize between the articles which need copy-editing and articles which don’t.
Using versatile methods of machine intelligence, we ease the load off the manual perusals and push content with light-speed efficiency. There’s an observable boost in productivity for copy-editors with this remarkable leap in the fields of grammar correction and content analysis. A substantial increase in the publication rate as well.
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