Named entity recognition (NER) is the task of identifying and tagging key information (entities) in text. Some possible entities are people, companies, years, and monetary values. NER is a form of natural language processing (NLP), a subfield of artificial intelligence. NLP is concerned with computers processing and analyzing natural language, i.e., any language that has developed naturally, rather than artificially, such as with computer coding languages.
What is a data program?
From a high-level perspective, a Data Program is a mechanism to transform any input (e.g. images, video, audio, etc.) to any output (e.g. classifications, categories, etc.). In order to achieve that it can route the data to different labeling sources. Labeling sources can be humans or machines. Parameters, like quality, cost and latency, define to which sources the data is routed.
What does the demo showcase?
The demo shows our NER Data Program in action. The video shortly explains how to provide labeling instructions for our Human & AI annotators, as well as showing the 'behind the scenes' labeling interface that our crowd is using to label your data.
What can I use this for?
You can use this labeled output data to train machine learning algorithms. NER can help improve customer support efforts (categorizing user requests, complaints and questions and filtering by priority keywords), tag and categorize news articles or blog posts, improve search engine results (improve the speed and relevance of search results and recommendations by summarizing descriptive text, reviews, and discussions), HR (speeding up the hiring process by summarizing applicants resumes)