Named Entity Recognition
Hello World,
Azure is puling up the curtains on another preview feature in Cognitive Services Text Analytics API – Named Entity Recognition
What it is?
You give the API free text and it will identify entities like people, locations, dates, numbers and more and bring the location,type and link to the information. very helpful and a big addition to the already existing features of the service
Text Analytics already can do:
- Detect language
- Analyze sentiment
- Extract key phrases
- Named Entity Recognition
In other words its a sort of LUIS but the intents are pre trained with types and sub types (in a table in the end of the article there is an example for the types that can be extracted.
Lior is working with Microsofts Cognitive Service every day from 9am
{ "Documents": [ { "Id": "1", "Entities": [ { "Name": "Lior", "Matches": [ { "Text": "Lior", "Offset": 0, "Length": 4 }], "Type": "Person" }, { "Name": "Microsoft", "Matches": [ { "Text": "Microsoft's", "Offset": 21, "Length": 11 }], "WikipediaLanguage": "en", "WikipediaId": "Microsoft", "WikipediaUrl": "https://en.wikipedia.org/wiki/Microsoft", "BingId": "a093e9b9-90f5-a3d5-c4b8-5855e1b01f85" }, { "Name": "Artificial intelligence", "Matches": [ { "Text": "AI", "Offset": 33, "Length": 2 }], "WikipediaLanguage": "en", "WikipediaId": "Artificial intelligence", "WikipediaUrl": "https://en.wikipedia.org/wiki/Artificial_intelligence", "BingId": "9d99fb44-edac-0e03-1579-19d8d8591a49" }, { "Name": "every day", "Matches": [ { "Text": "every day", "Offset": 36, "Length": 9 }], "Type": "DateTime", "SubType": "Set" }, { "Name": "9am", "Matches": [ { "Text": "9am", "Offset": 51, "Length": 3 }], "Type": "DateTime", "SubType": "Time" } ] }], "Errors": [] }
What do we get?
First detect that Lior is a Person – ☑ checked
Find Microsoft and mark it as an organization and give me the wiki about the company- ☑ checked
Finds the work AI trying to bring some information about that – ☑ checked
Extract date and time and identity it in the text – Very cool – ☑ checked
Conclusion:
Text Analytics is making progress and now include more features that help analyze any kind of text even in other languages
Cool
Supported Types for Named Entity Recognition:
Type | SubType | Example |
---|---|---|
Person | N/A* | "Jeff", "Ashish Makadia" |
Location | N/A* | "Redmond, Washington", "Paris" |
Organization | N/A* | "Microsoft" |
Quantity | Number | "6", "six" |
Quantity | Percentage | "50%", "fifty percent" |
Quantity | Ordinal | "2nd", "second" |
Quantity | NumberRange | "4 to 8" |
Quantity | Age | "90 days old", "30 years old" |
Quantity | Currency | "$10.99" |
Quantity | Dimension | "10 miles", "40 cm" |
Quantity | Temperature | "32 degrees" |
DateTime | N/A* | "6:30PM February 4, 2012" |
DateTime | Date | "May 2nd, 2017", "05/02/2017" |
DateTime | Time | "8am", "8:00" |
DateTime | DateRange | "May 2nd to May 5th" |
DateTime | TimeRange | "6pm to 7pm" |
DateTime | Duration | "1 minute and 45 seconds" |
DateTime | Set | "every Tuesday" |
DateTime | TimeZone | “UTC-7”, “CST” |
URL | N/A* | "http://www.bing.com" |
N/A* | "support@microsoft.com" |
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About The Author
Lior
Cloud Monitoring specialist, with more than 10 years of experience in providing brode wide monitoring solutions for SMP and Enterprises. I work at CloudValley, Microsoft's partner of the year 2015&2017 and largest MSP in Israel.