Pre-trained Entity Recognizers

Entity Recognition has a wide range of applications in the field of Natural Language Processing and Information Retrieval. The use cases for pre-trained Entity Recognizer are available with Smart Bots. 

• Resume Parser: This is a pretrained model on resumes collected from indeed.com, to extract entities from resume text such as Skills, Degree, Graduation Year, Location, Email Address, Designation, Years of Experience, Name, College Name, Companies worked. This pre-trained model gives better results for the resume documents in which the information is not organized into tables. 
• Generic: This is pretrained spacy model (en_core_web_sm) to extract generic entities. This recognizer will identify generic entities such as a person, organization, location, date, time, and others from the given text. 

  TYPE	DESCRIPTION
  PERSON	People, including fictional ones.
  NORP	Nationalities or religious or political groups.
  FAC	Buildings, airports, highways, bridges, etc.
  ORG	Companies, agencies, institutions, etc.
  GPE	Countries, cities, states.
  LOC	Non-GPE locations, mountain ranges, bodies of water.
  PRODUCT	Objects, vehicles, foods, etc. (Not services.)
  EVENT	Named hurricanes, battles, wars, sports events, etc.
  WORK_OF_ART	Titles of books, songs, etc.
  LAW	Named documents made into laws.
  LANGUAGE	Any named language.
  DATE	Absolute or relative dates or periods.
  TIME	Times shorter than a day.
  PERCENT	Percentage, including “%”.
  MONEY	Monetary values, including units.
  QUANTITY	Measurements, as of weight or distance.
  ORDINAL	“first”, “second”, etc.
  CARDINAL	Numerals that do not fall under another type.