Natural Language Understanding (NLU): Overview If we think about it, language is one of the most powerful tools in our arsenal.
Fast Stylometry Tutorial I’m introducing a Python library I’ve written, called faststylometry, which allows you to compare authors of texts by their writing style.
Image from: harmonydata.ac.uk “Thinking too much” I have been working on the development of Harmony, a tool to help psychology researchers harmonise questionnaire items in plain text across languages so that they can combine datasets from disparate sources.
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.
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.
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.
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.
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.
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.
What we can do for you
