Named-Entity Recognition For Your Text Notes
Named-Entity Recognition For Your Text Notes
Demo Video
A quick little demo from Tyler Bryden on named-entity recognition in Speak Ai text notes!
This unlocks remarkable potential for augmenting your writing experience and surfacing novel insights and patterns over time.
Thank you Vatsal Shah and Timothy Fosteman for your incredible work to bring this to life.
We hope you enjoy! ❤🤗
What is Named-Entity Recognition?
Data extraction task that is responsible for finding, storing and sorting textual content into default categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values and percentages.
Example: I am currently living in Toronto although I used to live in London and work every day out of Innovation Works.
What Does Speak Automatically Extract?
We are automatically extracting these categories below. If you want to find other entities in your audio and video, just send us a message and we will build it in for you!
PERSON | People, including fictional. |
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 smaller than a day. |
PERCENT | Percentage, including ”%“. |
MONEY | Monetary values, including unit. |
QUANTITY | Measurements, as of weight or distance. |
ORDINAL | “first”, “second”, etc. |
CARDINAL | Numerals that do not fall under another type. |
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