One of the primary ways that chatbots make sense of user queries is by using a technique called Entity Extraction. Basically text documents often contain names of people, locations, organizations or products. These are collectively called Named Entities. They can contain values like telephone numbers, links, currency amounts, email addresses, percentages and so on. These entities are source of interesting information about the text and can shed light on what the context of the text written in the document is. In order to extract these entities, the first step is identifying these named entities and values and then extracting them in the text analysis phase. Chatbots rely heavily on this technique and most chatbots deployed today use this either alone or in combination with other artificial intelligence technologies to decipher user responses.
So what exactly is Entity Extraction?
Entity extraction is a type of information extraction technique and is also known as Entity name extraction or named entity extraction. It is a process wherein key elements of text are identified and classified into pre-defined categories. This helps transform unstructured data into a structured one. This structured data is machine-readable and standard processing can be run over it to retrieve information, extract facts and answer questions.
Entity Extraction in Surbo
Surbo has a state of the art built in entity extractor that automatically identifies entities and determines their sentiment from the user input text. It detects companies, places, people, products, dates on its own. Surbo even has the feature of building your own custom entities which can allow to train your own machine learning models for discovery purposes.
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