@inproceedings{meng-etal-2023-crf,
title = "{CRF}-based recognition of invasive fungal infection concepts in {CHIFIR} clinical reports",
author = "Meng, Yang and
Rozova, Vlada and
Verspoor, Karin",
editor = "Muresan, Smaranda and
Chen, Vivian and
Casey, Kennington and
David, Vandyke and
Nina, Dethlefs and
Koji, Inoue and
Erik, Ekstedt and
Stefan, Ultes",
booktitle = "Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association",
month = nov,
year = "2023",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.alta-1.15",
pages = "130--135",
abstract = "Named entity recognition (NER) in clinical documentation is often hindered by the use of highly specialised terminology, variation in language used to express medical findings and general scarcity of high-quality data available for training. This short paper compares a Conditional Random Fields model to the previously established dictionary-based approach and evaluates its ability to extract information from a small corpus of annotated pathology reports. The results suggest that including token descriptors as well as contextual features significantly improves precision on several concept categories while maintaining the same level of recall.",
}
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<abstract>Named entity recognition (NER) in clinical documentation is often hindered by the use of highly specialised terminology, variation in language used to express medical findings and general scarcity of high-quality data available for training. This short paper compares a Conditional Random Fields model to the previously established dictionary-based approach and evaluates its ability to extract information from a small corpus of annotated pathology reports. The results suggest that including token descriptors as well as contextual features significantly improves precision on several concept categories while maintaining the same level of recall.</abstract>
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%0 Conference Proceedings
%T CRF-based recognition of invasive fungal infection concepts in CHIFIR clinical reports
%A Meng, Yang
%A Rozova, Vlada
%A Verspoor, Karin
%Y Muresan, Smaranda
%Y Chen, Vivian
%Y Casey, Kennington
%Y David, Vandyke
%Y Nina, Dethlefs
%Y Koji, Inoue
%Y Erik, Ekstedt
%Y Stefan, Ultes
%S Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association
%D 2023
%8 November
%I Association for Computational Linguistics
%C Melbourne, Australia
%F meng-etal-2023-crf
%X Named entity recognition (NER) in clinical documentation is often hindered by the use of highly specialised terminology, variation in language used to express medical findings and general scarcity of high-quality data available for training. This short paper compares a Conditional Random Fields model to the previously established dictionary-based approach and evaluates its ability to extract information from a small corpus of annotated pathology reports. The results suggest that including token descriptors as well as contextual features significantly improves precision on several concept categories while maintaining the same level of recall.
%U https://aclanthology.org/2023.alta-1.15
%P 130-135
Markdown (Informal)
[CRF-based recognition of invasive fungal infection concepts in CHIFIR clinical reports](https://aclanthology.org/2023.alta-1.15) (Meng et al., ALTA 2023)
ACL