Computer Science > Computation and Language
arXiv:2310.06165 (cs)
[Submitted on 9 Oct 2023 (v1), last revised 19 Oct 2023 (this version, v2)]
Title:CAW-coref: Conjunction-Aware Word-level Coreference Resolution
Authors:Karel D'Oosterlinck,Semere Kiros Bitew,Brandon Papineau,Christopher Potts,Thomas Demeester,Chris Develder
View a PDF of the paper titled CAW-coref: Conjunction-Aware Word-level Coreference Resolution, by Karel D'Oosterlinck and 5 other authors
View PDFAbstract: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 being much more efficient. In this work, we identify a routine yet important failure case of WL-coref: dealing with conjoined mentions such as 'Tom and Mary'. We offer a simple yet effective solution that improves the performance on the OntoNotes test set by 0.9% F1, shrinking the gap between efficient word-level coreference resolution and expensive SOTA approaches by 34.6%. Our Conjunction-Aware Word-level coreference model (CAW-coref) and code is available atthis https URL.
| Comments: | Accepted at CRAC 2023 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2310.06165 [cs.CL] |
| (orarXiv:2310.06165v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2310.06165 arXiv-issued DOI via DataCite |
Submission history
From: Karel D'Oosterlinck [view email][v1] Mon, 9 Oct 2023 21:32:49 UTC (82 KB)
[v2] Thu, 19 Oct 2023 17:31:14 UTC (82 KB)
Full-text links:
Access Paper:
- View PDF
- TeX Source
- Other Formats
View a PDF of the paper titled CAW-coref: Conjunction-Aware Word-level Coreference Resolution, by Karel D'Oosterlinck and 5 other authors
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.