Entity extraction is, in the context of search, the process of figuring out which fields a query should target, as opposed to always hitting all fields. The reason we may want to involve entity extraction in search is to improve precision. For example: how do we tell that, when the user typed in Apple iPhone, the intent was to run company:Apple AND product:iPhone? And not bring back phone stickers in the shape of an apple?
Entity extraction is, in the context of search, the process of figuring out which fields a query should target, as opposed to always hitting all fields. The reason we may want to involve entity extraction in search is to improve precision. For example: how do we tell that, when the user typed in Apple iPhone, the intent was to run company:Apple AND product:iPhone? And not bring back phone stickers in the shape of an apple?
As shown in Elasticsearch Key Metrics, the setup, tuning, and operations of Elasticsearch require deep insights into the performance metrics such as index rate, query rate, query latency, merge times, and many more. Sematext provides an excellent alternative to other Elasticsearch monitoring tools.
Over the years the adoption of Elasticsearch and its ecosystem of tools positioned them as the leaders in the time series data management and analysis market. With strong search capabilities, great analytical engine, Kibana as the flexible frontend and a number of data shippers enable building of end to end data processing pipeline using components designed to work with each other. Very simple setup and configuration resulted in high adoption rates and the whole stack gaining more and more users.
Entity extraction is the process of figuring out which fields a query should target, as opposed to always hitting all fields. For example: how to tell, when the user typed in Apple iPhone, that the intent was to run company:Apple AND product:iPhone?
Over the years, natural language processing, in the world of search, went from interesting detail to a must have, especially in areas such as e-commerce. Engineers started incorporating classification, synonym generation, named entity recognition and much more into their search systems giving users better search results and in some cases leading to more revenue.
