Text data is a highly insightful and highly available data source that companies often undervalue. Part of a growing subset of Artificial Intelligence (AI), Conversational AI uses a combination of Natural Language Processing, Machine Learning, Neural Networks and Contextual Awareness to process, understand and extract value from text data.
Processing text accurately requires rigorous cleansing in order to ensure that your data models are accurate. You cannot simply go from raw text to establishing an effective data model without heeding the ‘garbage in = garbage out’ mantra.
While it’s important to know that text cleaning is task specific, after working with many enterprises building Intelligent Assistants that automate sales and support, we’ve identified 7 essential steps you should take in order to get crisply clean data to use in your machine learning models.
Most developers use Python, tools like NLTK and spend time searching for large open source libraries to perform these steps. But, for developers who work with English and Bahasa Malaysia language texts, Dialex makes this even easier.
We’ve partnered with RapidAPI to help developers limit the time spent on cleaning text data and maximise the time spent on building amazing machine learning models.
RapidAPI is a marketplace for developers to find and connect to APIs. Created by developers for developers, the San Francisco-based company believes in a world with connected software, and they value APIs because they allow software programs to talk to each other. When software programs can connect to each other, they become infinitely more powerful.
Search through their marketplace for complimentary API’s that make Dialex ever more powerful. For example, add to a Voice-to-text API to ensure that voice data is transcribed cleanly; or, combine with a Machine Learning Packages to rapidly classify your text.