I’m sure you will have seen news articles and social media posts about the recent generation of language models which are able to generate human-like text.
We designed the Clinical Trial Risk Tool, a risk assessment tool using AI and NLP to quantify the risk of a trial ending uninformatively.
A tour of the challenges you encounter when using natural language processing on multilingual data. Most of the projects that I take on involve unstructured text data in English only, but recently I have seen more and more projects involving text in different languages, often all mixed together.
We have been developing a tool using Natural Language Processing which is designed to help researchers in the social sciences to harmonise datasets from different contexts.
We excited to announce that we are working on a new project called Harmony for the Wellcome Trust Mental Health Data Prize - this uses natural language processing to combine data in the social sciences from different questionnaires, such as the GAD-7 and Beck’s Anxiety Inventory, even when the questionnaires are in different languages!
Before we answer one of the most commonly searched questions on Google – “What is an NLP expert?” – we ought to understand what NLP is.
Can symbolic AI, or machine learning, or hybrid AI (a mixture of the two), be used for legal reasoning? I have been looking into the problem of legal reasoning with AI.
Natural language processing or NLP is the area of artificial intelligence to do with analysing human language. Natural language processing is an emerging field with a huge number of business applications.
How Hybrid AI can combine the best of symbolic AI and machine learning to predict salaries, clinical trial risk and costs, and enhance chatbots.
Natural language processing (NLP) is the science of getting computers to talk, or interact with humans in human language. Examples of natural language processing include speech recognition, spell check, autocomplete, chatbots, and search engines.
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