Interestingly enough, paperclips have their own day of honor. On May 29th we celebrate #NationalPaperclipDay! That well-known piece of curved wire deserves attention for keeping our papers together and helping us stay organized. Do you remember who else deserved the same attention? Clippit – the infamous Microsoft Office assistant, popularly known as ‘Clippy’.
What can we learn from Clippy about AI? was published on SAS Users.
This blog shows how the automatically generated concepts and categories in Visual Text Analytics (VTA) can be refined using LITI and Boolean rules. I will use a data set that contains information on 1527 randomly selected movies: their titles, reviews, MPAA Ratings, Main Genre classifications and Viewer Ratings.
Analysis of Movie Reviews using Visual Text Analytics was published on SAS Users.
Imagine a world where satisfying human-computer dialogues exist. With the resurgence of interest in natural language processing (NLP) and understanding (NLU) – that day may not be far off. In order to provide more satisfying interactions with machines, researchers are designing smart systems that use artificial intelligence (AI) to develop […]
Moving from natural language processing to natural language understanding was published on SAS Users.
Natural language understanding (NLU) is a subfield of natural language processing (NLP) that enables machine reading comprehension. While both understand human language, NLU goes beyond the structural understanding of language to interpret intent, resolve context and word ambiguity, and even generate human language on its own. NLU is designed for […]
So, you’ve figured out NLP but what’s NLU? was published on SAS Users.
There is tremendous value buried text sources such as call center and chat dialogues, survey comments, product reviews, technical notes, legal contracts… How can we extract the signal we want amidst all the noise?
Automatically extracting key information from textual data was published on SAS Users.
Amidst the growing popularity of modern machine learning and deep learning techniques, one of the biggest challenges is the ability to obtain large amounts of training data suitable for your use case. This post discusses how the analytical approach for Named Entity Recognition (NER) can help.
Reduce the cost-barrier of generating labeled text data for machine learning algorithms was published on SAS Users.