Difference between Named Entity Recognition and Entity Resolution

A named entity is a real world object which can be denoted through a proper name. Named entity can be persons, organisations, countries, currencies etc. When we look at text in the form of sentences or paragraphs, different entities may be mentioned in them. For example:

Sachin played a spectacular match at the Eden Gardens today.

Here, Sachin and Eden Garden are named entities standing for person and place respectively.

Or

Github has a free and open source entity resolution software repository named Zingg.

Here, Github and Zinggare named entities representing company and name of software.

Named Entity Recognition is typically done through Natural Language Processing. One earlier technique involved tagging parts of speech to identify nouns and then identifying entity types through pattern matching. A more comprehensive survey of techniques for NER can be found here

NER helps in understanding text, question answering, grouping together relevant information about entities for news, analysis etc.

Entity Resolution on the other hand is linking the same entity in different records where a common identifier is missing. Entity Resolution works on structured text in most cases, like customer or company records, though it may also be applied to long texts like product names and descriptions.

Suppose there is a hospital registering a patient. Typical information collected would be first and last names, address, telephone number and date of birth.

First Name: Anne

Last Name: Smith

Address: 123, Milwauke Dr, Connecticut

Phone: (123) 456 7890

The patient may undergo treatment and build a case history. After a few years, the person needs treatment for a new ailment and reappears at the hospital. Her telephone number may have changed, and this time the details may get captured as

First Name: Ann

Last Name: Smith

Address: 123 Milwauke Drive, Connecticut

Phone: (213) 645 7908

However, it is important for the hospital to know that these are same individuals. But slight variations in her details need to be reconciled, so that an effective case history can be built. This linkage is known as entity resolution. Check a few more samples and challenges here.

Entity Resolution is typically done through rule based systems, though recently a lot of work has happened on the ML/AI based approaches including deep learning.

Entity Resolution is a fundamental technique used in medical research, census data analysis, CRM record deduplication, householding and other analytics and data quality functions.