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:
- Oceania > Australia
- North America
- Dominican Republic (0.04)
- United States
- Maryland > Baltimore (0.04)
- Washington > King County
- Seattle (0.28)
- Texas > Travis County
- Austin (0.04)
- New York > New York County
- New York City (0.04)
- Minnesota > Hennepin County
- Minneapolis (0.14)
- Maine > Kennebec County
- Waterville (0.04)
- Indiana > St. Joseph County
- Notre Dame (0.04)
- California > San Francisco County
- San Francisco (0.14)
- Canada > British Columbia
- Europe
- Germany > Berlin (0.04)
- Russia (0.04)
- Ukraine (0.04)
- Italy > Tuscany
- Florence (0.04)
- Denmark > Capital Region
- Copenhagen (0.04)
- Portugal > Lisbon
- Lisbon (0.04)
- Spain > Catalonia
- Barcelona Province > Barcelona (0.04)
- Ireland > Leinster
- County Dublin > Dublin (0.04)
- United Kingdom
- Scotland > City of Edinburgh
- Edinburgh (0.04)
- England > Cambridgeshire
- Cambridge (0.04)
- Scotland > City of Edinburgh
- Belgium > Brussels-Capital Region
- Brussels (0.04)
- Asia
- China > Hong Kong (0.04)
- Russia (0.04)
- Middle East
- Japan > Honshū
- Kansai > Osaka Prefecture > Osaka (0.04)
- Genre:
- Overview (0.94)
- Industry:
- Information Technology (1.00)
- Education (0.68)
- Technology: