Span-Oriented Information Extraction -- A Unifying Perspective on Information Extraction
Ding, Yifan, Yankoski, Michael, Weninger, Tim
–arXiv.org Artificial Intelligence
Information Extraction refers to a collection of tasks within Natural Language Processing (NLP) that identifies sub-sequences within text and their labels. These tasks have been used for many years to link extract relevant information and to link free text to structured data. However, the heterogeneity among information extraction tasks impedes progress in this area. We therefore offer a unifying perspective centered on what we define to be spans in text.
arXiv.org Artificial Intelligence
Mar-18-2024
- Country:
- Asia
- China > Hong Kong (0.04)
- Japan > Honshū
- Kansai > Osaka Prefecture > Osaka (0.04)
- Middle East
- Russia (0.04)
- Europe
- Belgium > Brussels-Capital Region
- Brussels (0.04)
- United Kingdom
- England > Cambridgeshire
- Cambridge (0.04)
- Scotland > City of Edinburgh
- Edinburgh (0.04)
- England > Cambridgeshire
- Ireland > Leinster
- County Dublin > Dublin (0.04)
- Ukraine (0.04)
- Spain > Catalonia
- Barcelona Province > Barcelona (0.04)
- Russia (0.04)
- Portugal > Lisbon
- Lisbon (0.04)
- Denmark > Capital Region
- Copenhagen (0.04)
- Italy > Tuscany
- Florence (0.04)
- Germany > Berlin (0.04)
- Belgium > Brussels-Capital Region
- North America
- Canada > British Columbia
- Dominican Republic (0.04)
- United States
- California > San Francisco County
- San Francisco (0.14)
- Indiana > St. Joseph County
- Notre Dame (0.04)
- Maine > Kennebec County
- Waterville (0.04)
- Maryland > Baltimore (0.04)
- Minnesota > Hennepin County
- Minneapolis (0.14)
- New York > New York County
- New York City (0.04)
- Texas > Travis County
- Austin (0.04)
- Washington > King County
- Seattle (0.28)
- California > San Francisco County
- Oceania > Australia
- Asia
- Genre:
- Overview (0.94)
- Industry:
- Education (0.68)
- Information Technology (1.00)
- Technology: