What is Natural Language Processing?
Natural Language Processing (NLP) is a field of Artificial Intelligence that gives computer programs the ability to read, understand and derive meaning from human languages.
The types of data associated with NLP are normally unstructured text narratives, which do not fit into a conventional row/column format on a spreadsheet or database.
What Does NLP Try to Solve?
Our brains have an innate ability to catalogue, retrieve, comprehend, contextualise, summarise and detect nuances in languages.
NLP's goal is to use AI to replicate the brain's language functionality.
Text Classification or Categorisation
Text classification is the process of applying categories or classifications according to its content.
Some examples of real world applications are social media monitoring like classifying tweet toxicity and Legal contract reviews like classifying clause fairness.
Question Answering
Question Answering involves building systems that have the ability to retrieve a catalogue of text information and answer questions posed by humans in a natural language fashion.
Siri and Alexa are some familiar examples. The technology can be further extended to domain specific industries like the health care and finance sectors.
Multiple Choice Question & Answering
On face value, it might seem like a purely academic pursuit. But answering questions which require reasoning, and picking the best solution out of possible outcomes, based on prior knowledge, has many potential real world applications (think about the dystopian movie Minority Report):
Health: Best treatment based on a patient’s diagnosis;
Legal: Predict the outcome of a legal case based on multiple scenarios;
Marketing/Advertising: Custom advertising based on a customer's profile;
Customer service: Next best action based on a conversation with a customer, or chatbots/assistants;
Creative: Movie script generation - Select the best movie ending scene
Text Summarisation
Text summarization refers to the technique of shortening long pieces of text. This helps reduce reading time, and focuses on important extracts of information in a large corpus of text.
Named Entity Recognition (NER)
NER is a process of automatically identifying named entities in a text and classifying them into predefined categories. Entities can be names of people, organizations, locations, times, quantities etc.
Some real world applications include anonymization of personal information (names, addresses etc), and enhancing access to a museum's catalogue
NoamAi
NoamAi is a fully automated NLP platform that embodies the principles of DevOps and CI/CD (MLOps) for Machine Learning and AI. It simplifies and automates the end to end ML model build process (data preparation -> model training -> model deployment) by way of standardisation, consistency, versioning, speed and scale. All users need to do is to provide the data and define the problem.
The platform can build NLP models for the following:
Text Classification or Categorisation
Question Answering
Multiple Choice Question & Answering
Text Summarisation
Named Entity Recognition (NER)