We designed the Clinical Trial Risk Tool, a clinical trial risk assessment tool using AI and NLP to quantify the risk of a trial ending uninformatively. Get in touch with us if you need custom AI strategy consulting for healthcare.
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. This makes for a fun challenge.
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. Large companies which have their own data science team often do not have NLP specialists in-house and may need to bring NLP experts as consultants.
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.
What is sentiment analysis and what are the key trends in sentiment analysis today? Understand and try out some of the simplest and most cutting-edge sentiment analysis technologies!
Named entity recognition (NER) is the task of recognising proper names and words from a special class in a document, such as product names, locations, people, or diseases. This can be compared to the related task of named entity linking, where the products are linked to a unique ID.
Does protecting sensitive data mean that you also need to compromise the performance of your machine learning model? If you study machine learning in university, or take an online course, you will normally work with a set of publicly available datasets such as the Titanic Dataset, Fisher’s Iris Flower Dataset, or the Labelled Faces in the Wild Dataset. For example, you may train a face recognition model on a set of celebrity faces that are already in the public domain, rather than private or sensitive data such as CCTV images. These public datasets have often been around a very long time and serve as useful benchmarks that everyone agrees on.
How NLP document similarity algorithms can be used to find similar documents and build document recommendation systems. Let us imagine that you are reading a document, and would like to find which other documents in a database are most similar to it. This is called the document similarity problem, or the semantic similarity problem.
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