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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…
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Improving how mixed sources of data are accurately merged together through the use of fuzzy joins

In our daily work we often need to combine two or more datasets together into one. This type of operation, known as a join, is rather simple when each record contains a unique ID present in both datasets. However, there are many scenarios where datasets use different methods of creating unique keys and thus do not match or do not have unique keys at all. In these situations the traditional…
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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…
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PolyAnalyst introduces support for using stop list dictionaries when analyzing spelling errors

In previous builds of PolyAnalyst the only way to stop a word from showing up in the spell checker was to add it to the morphology dictionary. This would sometimes result in having to add things to the morphology dictionary that might not really belong there, such as product codes (Model ABC-XYZ) and the occasional single foreign word (e.g. yukata). In order to prevent this dictionary mismatch PolyAnalyst now includes…
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