@inproceedings{bell-etal-2016-sieve,
title = "Sieve-based Coreference Resolution in the Biomedical Domain",
author = "Bell, Dane and
Hahn-Powell, Gus and
Valenzuela-Esc{\'a}rcega, Marco A. and
Surdeanu, Mihai",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Goggi, Sara and
Grobelnik, Marko and
Maegaard, Bente and
Mariani, Joseph and
Mazo, Helene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro{\v{z}}, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L16-1027",
pages = "177--183",
abstract = "We describe challenges and advantages unique to coreference resolution in the biomedical domain, and a sieve-based architecture that leverages domain knowledge for both entity and event coreference resolution. Domain-general coreference resolution algorithms perform poorly on biomedical documents, because the cues they rely on such as gender are largely absent in this domain, and because they do not encode domain-specific knowledge such as the number and type of participants required in chemical reactions. Moreover, it is difficult to directly encode this knowledge into most coreference resolution algorithms because they are not rule-based. Our rule-based architecture uses sequentially applied hand-designed {``}sieves{''}, with the output of each sieve informing and constraining subsequent sieves. This architecture provides a 3.2{\%} increase in throughput to our Reach event extraction system with precision parallel to that of the stricter system that relies solely on syntactic patterns for extraction.",
}
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%0 Conference Proceedings
%T Sieve-based Coreference Resolution in the Biomedical Domain
%A Bell, Dane
%A Hahn-Powell, Gus
%A Valenzuela-Escárcega, Marco A.
%A Surdeanu, Mihai
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Grobelnik, Marko
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Helene
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 May
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F bell-etal-2016-sieve
%X We describe challenges and advantages unique to coreference resolution in the biomedical domain, and a sieve-based architecture that leverages domain knowledge for both entity and event coreference resolution. Domain-general coreference resolution algorithms perform poorly on biomedical documents, because the cues they rely on such as gender are largely absent in this domain, and because they do not encode domain-specific knowledge such as the number and type of participants required in chemical reactions. Moreover, it is difficult to directly encode this knowledge into most coreference resolution algorithms because they are not rule-based. Our rule-based architecture uses sequentially applied hand-designed “sieves”, with the output of each sieve informing and constraining subsequent sieves. This architecture provides a 3.2% increase in throughput to our Reach event extraction system with precision parallel to that of the stricter system that relies solely on syntactic patterns for extraction.
%U https://aclanthology.org/L16-1027
%P 177-183
Markdown (Informal)
[Sieve-based Coreference Resolution in the Biomedical Domain](https://aclanthology.org/L16-1027) (Bell et al., LREC 2016)
ACL
- Dane Bell, Gus Hahn-Powell, Marco A. Valenzuela-Escárcega, and Mihai Surdeanu. 2016. Sieve-based Coreference Resolution in the Biomedical Domain. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 177–183, Portorož, Slovenia. European Language Resources Association (ELRA).