In the first stage, we begin by collecting data from multiple sources and building a raw text corpus. Damaged, irrelevant or incomplete data is eliminated and useful text is normalized and prepared for further analysis.
Text Parsing and Exploratory Data Analysis
This is the structuring stage where the raw data is sifted and organized to do a more focused analysis with a smaller dataset. This involves identifying and removing irrelevant sections, extracting coded metadata and determining the format. By selecting the various intents and entities required for the predetermined tasks, a deep exploratory analysis helps establish a format for representation.
Text Representation and Transformation
Now that the datasets are categorized, we use various visualization techniques to represent the data in a meaningful format to retrieve useful insights. This includes a semantic, syntactic and pragmatic analysis of the text to get an overview of the interpretable content.
We now approach the most important Natural Language Processing discipline of modeling artificial neural networks (ANN) and training them to automate the learning of complex linguistic and behavioral models. Text mining at this stage helps to funnel down the data and do targeted information retrieval.
Evaluation and Deployment
At the final stage, the NLP model is tested for performance against a number of training parameters. The metrics are observed and corrective measures are taken where necessary. The successful model is then deployed in the execution environment.