|
--- |
|
license: other |
|
--- |
|
|
|
# Phrase Detectives Version 3 |
|
|
|
- **Homepage:** [CoNLL-2012 Shared Task](https://conll.cemantix.org/2012/data.html), [Author's page](https://cemantix.org/data/ontonotes.html) |
|
- **Repository:** [Mendeley](https://data.mendeley.com/datasets/zmycy7t9h9) |
|
- **Conversion:** https://github.com/vdobrovolskii/wl-coref/commit/4af0aa04eefad5b68a1fb6ca48a846a449bfa4b0 |
|
|
|
## Details |
|
|
|
The original consistuency parse annotations of `coref-data/conll2012_raw` converted to conllu dependency parses using `convert_to_heads.py` from https://github.com/vdobrovolskii/wl-coref |
|
|
|
## Citations |
|
``` |
|
@inproceedings{dobrovolskii-2021-word, |
|
title = "Word-Level Coreference Resolution", |
|
author = "Dobrovolskii, Vladimir", |
|
editor = "Moens, Marie-Francine and |
|
Huang, Xuanjing and |
|
Specia, Lucia and |
|
Yih, Scott Wen-tau", |
|
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", |
|
month = nov, |
|
year = "2021", |
|
address = "Online and Punta Cana, Dominican Republic", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://aclanthology.org/2021.emnlp-main.605", |
|
doi = "10.18653/v1/2021.emnlp-main.605", |
|
pages = "7670--7675", |
|
abstract = "Recent coreference resolution models rely heavily on span representations to find coreference links between word spans. As the number of spans is $O(n^2)$ in the length of text and the number of potential links is $O(n^4)$, various pruning techniques are necessary to make this approach computationally feasible. We propose instead to consider coreference links between individual words rather than word spans and then reconstruct the word spans. This reduces the complexity of the coreference model to $O(n^2)$ and allows it to consider all potential mentions without pruning any of them out. We also demonstrate that, with these changes, SpanBERT for coreference resolution will be significantly outperformed by RoBERTa. While being highly efficient, our model performs competitively with recent coreference resolution systems on the OntoNotes benchmark.", |
|
} |
|
|
|
@inproceedings{pradhan-etal-2013-towards, |
|
title = "Towards Robust Linguistic Analysis using {O}nto{N}otes", |
|
author = {Pradhan, Sameer and |
|
Moschitti, Alessandro and |
|
Xue, Nianwen and |
|
Ng, Hwee Tou and |
|
Bj{\"o}rkelund, Anders and |
|
Uryupina, Olga and |
|
Zhang, Yuchen and |
|
Zhong, Zhi}, |
|
booktitle = "Proceedings of the Seventeenth Conference on Computational Natural Language Learning", |
|
month = aug, |
|
year = "2013", |
|
address = "Sofia, Bulgaria", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://aclanthology.org/W13-3516", |
|
pages = "143--152", |
|
} |
|
``` |
