Summary of the paper
| Title | Corpus for Coreference Resolution on Scientific Papers |
|---|---|
| Authors | Panot Chaimongkol, Akiko Aizawa and Yuka Tateisi |
| Abstract | The ever-growing number of published scientific papers prompts the need for automatic knowledge extraction to help scientists keep up with the state-of-the-art in their respective fields. To construct a good knowledge extraction system, annotated corpora in the scientific domain are required to train machine learning models. As described in this paper, we have constructed an annotated corpus for coreference resolution in multiple scientific domains, based on an existing corpus. We have modified the annotation scheme from Message Understanding Conference to better suit scientific texts. Then we applied that to the corpus. The annotated corpus is then compared with corpora in general domains in terms of distribution of resolution classes and performance of the Stanford Dcoref coreference resolver. Through these comparisons, we have demonstrated quantitatively that our manually annotated corpus differs from a general-domain corpus, which suggests deep differences between general-domain texts and scientific texts and which shows that different approaches can be made to tackle coreference resolution for general texts and scientific texts. |
| Topics | Anaphora, Coreference, Other |
Full paper  |
Corpus for Coreference Resolution on Scientific Papers |
| Bibtex | @InProceedings{CHAIMONGKOL14.286, author = {Panot Chaimongkol and Akiko Aizawa and Yuka Tateisi}, title = {Corpus for Coreference Resolution on Scientific Papers}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |