CAW-coref: Conjunction-Aware Word-level Coreference Resolution
D'Oosterlinck, Karel, Bitew, Semere Kiros, Papineau, Brandon, Potts, Christopher, Demeester, Thomas, Develder, Chris
–arXiv.org Artificial Intelligence
State-of-the-art coreference resolutions systems depend on multiple LLM calls per document and are thus prohibitively expensive for many use cases (e.g., information extraction with large corpora). The leading word-level coreference system (WL-coref) attains 96.6% of these SOTA systems' performance while Figure 1: We identify two types of failure cases for being much more efficient. In this work, we WL-coref when processing conjoined mentions. Our identify a routine yet important failure case of simple solution, CAW-coref, addresses these errors. WL-coref: dealing with conjoined mentions such as Tom and Mary.
arXiv.org Artificial Intelligence
Oct-19-2023
- Country:
- North America
- United States
- Maryland
- Howard County > Columbia (0.04)
- Baltimore (0.04)
- Louisiana > Orleans Parish
- New Orleans (0.04)
- California > Orange County
- Irvine (0.04)
- Maryland
- Canada > British Columbia
- United States
- Europe
- Italy (0.04)
- Denmark > Capital Region
- Copenhagen (0.04)
- Asia
- South Korea (0.04)
- Taiwan > Taiwan Province
- Taipei (0.04)
- North America
- Genre:
- Research Report (0.64)
- Technology: