Entity Resolution in Text Analysis
In our analysis of textual data there are many different types of entities we need to identify and capture such as companies, organizations, places, currencies, stocks, and people. This task alone can be challenging, but in many scenarios it is further complicated by the fact that entities can be highly contextual and can be referenced in a myriad of forms. Referencing people easily demonstrates this phenomenon. For example, the individual whose name is Steve Michael Johnson may be referred to as Steve, Steve Johnson, Mr. Johnson, and in other ways. However, this is only the beginning of the complexity. All of these references are simply versions of the full name and so they are not too difficult to link together. However, depending on the context, an individual may be referenced in a manner using no fragment of their human name. Consider auto accident reports. In this data an individual may…
