Contact Us

Normalization in preparation for fuzzy matching

When we need to compare entities stored as string data we often need to consider many different pieces of the string. For example, suppose we examine the following names in our data: Steven M Johnson Steve Michael Johnson When we, as humans, compare these entities, we don’t directly compare each string as a whole. Instead we compare substrings. “Steven” is compared to “Steve,” “M” to “Michael,” and “Johnson” to “Johnson.”…
Read More

Fuzzy matching

In our daily work we often need to make comparisons of data between and within datasets. Such operations include looking for multiple copies of records for elimination or some other analysis task. Another type of operation is a “join” which is a method of combining two tables into one. For example, we may have one table that contains products and their prices while another table contains products and the current…
Read More

Challenges in human name detection

When working with big data, it is often possible to automate the slow, mind-numbing information extraction tasks, for precisely the reason that they require little creative energy. One such automatable process is detecting, extracting, and resolving human names from text data. For example, an insurance company may want to automatically extract from call center notes the names of all parties involved in an accident. However, they process hundreds of thousands…
Read More

What is sentiment analysis?

Sentiment Analysis is another buzzword that we hear more and more often, but may not entirely understand what it does or how to use it. Sentiment Analysis application in business offers benefits to both companies and customers, such as improving products and services, identifying strengths and weaknesses of the competition, as well as targeted advertising. Sentiment Analysis, also known as Sentiment Detection or Opinion Mining, is the classification of the…
Read More
Menu