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
Tag: natural language processing
Recently, the North Carolina Human Trafficking Commission hosted a regional symposium to help strengthen North Carolina’s multidisciplinary response to human trafficking. One of the speakers shared an anecdote from a busy young woman with kids. She had returned home from work and was preparing for dinner; her young son wanted
Structuring a highly unstructured data source Human language is astoundingly complex and diverse. We express ourselves in infinite ways. It can be very difficult to model and extract meaning from both written and spoken language. Usually the most meaningful analysis uses a number of techniques. While supervised and unsupervised learning,
I look forward to Pi Day every year at SAS because it's a day of celebration including yummy pies and challenging games that challenge you to recall the digits after the decimal point in Pi. Plus you get to wear Pi t-shirts (I have about seven). Today, though we are going to talk about DLPy which sounds like Pi and
The Special Olympics is part of the inclusion movement for people with intellectual disabilities. The organisation provides year-round sports training and competitions for adults and children with intellectual disabilities. In March 2019 the Special Olympics World Games will be held in Abu Dhabi, United Arab Emirates. SAS is an official
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?
When it comes to forecasting new product launches, executives say that it's a frustrating, almost futile, effort. The reason? Minimal data, limited analytic capabilities and a general uncertainty surrounding a new product launch. Not to mention the ever-changing marketplace. Nevertheless, companies cannot disregard the need for a new product forecast
Every day, military intelligence analysts sit behind computers reading a never-ending stream of reports, updating presentation templates and writing assessments. But intelligence is more than documenting events and sharing breaking news. It involves understanding and predicting complexities in human behavior across various organizational constructs and using facets of information to
Regular expressions are a powerful method for finding specific patterns in text. The syntax of regular expressions is intimidating, but once you've solved a few pattern-recognition problems with regex, you'll never go back to your old methods.
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.
Deep learning has taken off because organizations of all sizes are capturing a greater variety of data and can mine bigger data, including unstructured data. It’s not just large companies like Amazon, SAS and Google that have access to big data. It’s everywhere. Deep learning needs big data, and now
Word Mover's Distance (WMD) is a distance metric used to measure the dissimilarity between two documents, and its application in text analytics was introduced by a research group from Washington University in 2015. The group's paper, From Word Embeddings To Document Distances, was published on the 32nd International Conference on Machine
My local middle school publishes a weekly paper. Very recently, I noted an article in that paper regarding an expose on human trafficking overseas, "World Slavery: The Terrors Our World Tries to Forget." The eloquent article in part highlighted how children have been exploited in the fishing industry in Ghana
Maybe you’ve heard of text analytics (or natural language processing) as a way to analyze consumer sentiment. Businesses often use these techniques to analyze customer complaints or comments on social media, to identify when a response is needed. But text analytics has far more to offer than examining posts on
As a former intelligence analyst, I can't help but breathe a huge sigh of frustration. The special AI "task forces" and their massive budgets are great, but it's time to get honest about the rest of the military. Ask any every day soldier, sailor, airman or Marine their opinion of
A chatbot is a computer program that uses natural language processing (NLP) and artificial intelligence to simulate human conversation and derive a response. Essentially, it’s a machine that can chat with you or respond to your chatter. Chatbots can save time and money when used to handle simple, automated tasks.
Small causes can have large effects; or how a discovery in the Barnett Shale can spike some interest in the rest of the world and change the face of the industry. This article is co-written by Sylvie Jacquet-Faucillon, Senior Analytics Presales Consultant, SAS France; and David Dozoul, Senior Adviser
Don’t get me wrong. I have no doubt in the capabilities of our SAS products and SAS solutions! But I wanted to get a firsthand experience of our new solution for text analytics, SAS Contextual Analysis 14.1. And the result is very convincing! But let’s start from the beginning. Functions
This is the first of two articles looking at how to listen to what your customers are saying and act upon it – that is, how to understand the voice of the customer. Over the last few years, one of the big uses for SAS® Text Analytics has been to
Is cognitive computing an application of text mining? If you have asked this question, you are not alone. In fact, lately I have heard it quite often. So what is cognitive computing, really? A cognitive computing system, as stated by Dr. John E. Kelly III, is one that has the
Hi, there! First of all, let me introduce myself, as this is my first blog. I am Simran Bagga, and three weeks ago I became the Product Manager for Text Analytics at SAS. This role might be new to me, but text analytics is not. For the past 12 years,
In today’s world of instant gratification, consumers want – and expect – immediate answers to their questions. Quite often, that help comes in the form of a live chat session with a customer service agent. The logs from these chats provide a unique analysis opportunity. Like a call center transcript,
Recently, I have been thinking about how search can play more of a part in discovery and exploration with SAS Text Miner. Unsupervised text discovery usually begins with a look at the frequent or highly weighted terms in the collection, perhaps includes some edits to the synonym and stop lists,
The benefits of big data often depend on taming unstructured data. However, in international contexts, customer comments, employee notes, external websites, and the social media labyrinth are not exclusively written in English, or any single language for that matter. The Tower of Babel lives and it is in your unstructured
When I ask people what they know about Denmark they often mention Hans Christian Andersen. He was born in Denmark in 1805 and is one of the most adored children’s authors of all time. Many of his fairy tales are known worldwide as they have been translated into more than
~ This article is co-authored by Biljana Belamaric Wilsey and Teresa Jade, both of whom are linguists in SAS' Text Analytics R&D. When I learned to program in Python, I was reminded that you have to tell the computer everything explicitly; it does not understand the human world of nuance
Double negatives seem to be everywhere, I have noticed them a lot in music recently. Since Pink Floyd sang "We don't need no education", to Rihanna's "I wasn’t looking for nobody when you looked my way". My own favourite song with a double negative is "I can't get no sleep" - Faithless. This
Today’s natural language processing (NLP) systems can do some amazing things, including enabling the transformation of unstructured data into structured numerical and/or categorical data. Why is this important? Because once the key information has been identified or a key pattern modeled, the newly created, structured data can be used in