@inproceedings{lal-etal-2025-lens,
title = "{LENS}: Learning Entities from Narratives of Skin Cancer",
author = "Lal, Daisy Monika and
Rayson, Paul and
Peter, Christopher and
Ezeani, Ignatius and
El-Haj, Mo and
Zhu, Yafei and
Liu, Yufeng",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven and
Mather, Brodie and
Dras, Mark",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: System Demonstrations",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-demos.3/",
pages = "20--27",
abstract = "Learning entities from narratives of skin cancer (LENS) is an automatic entity recognition system built on colloquial writings from skin cancer-related Reddit forums. LENS encapsulates a comprehensive set of 24 labels that address clinical, demographic, and psychosocial aspects of skin cancer. Furthermore, we release LENS as a PyPI and pip package, making it easy for developers to download and install, and also provide a web application that allows users to get model predictions interactively, useful for researchers and individuals with minimal programming experience. Additionally, we publish the annotation guidelines designed specifically for spontaneous skin cancer narratives, that can be implemented to better understand and address challenges when developing corpora or systems for similar diseases. The model achieves an overall entity-level F1 score of 0.561, with notable performance for entities such as {\textquotedblleft}CANC{\_}T{\textquotedblright} (0.747), {\textquotedblleft}STG{\textquotedblright} (0.788), {\textquotedblleft}POB{\textquotedblright} (0.714), {\textquotedblleft}GENDER{\textquotedblright} (0.750), {\textquotedblleft}A/G{\textquotedblright} (0.714), and {\textquotedblleft}PPL{\textquotedblright} (0.703). Other entities with significant results include {\textquotedblleft}TRT{\textquotedblright} (0.625), {\textquotedblleft}MED{\textquotedblright} (0.606), {\textquotedblleft}AGE{\textquotedblright} (0.646), {\textquotedblleft}EMO{\textquotedblright} (0.619), and {\textquotedblleft}MHD{\textquotedblright} (0.5). We believe that LENS can serve as an essential tool supporting the analysis of patient discussions leading to improvements in the design and development of modern smart healthcare technologies."
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="lal-etal-2025-lens">
<titleInfo>
<title>LENS: Learning Entities from Narratives of Skin Cancer</title>
</titleInfo>
<name type="personal">
<namePart type="given">Daisy</namePart>
<namePart type="given">Monika</namePart>
<namePart type="family">Lal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Paul</namePart>
<namePart type="family">Rayson</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Christopher</namePart>
<namePart type="family">Peter</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ignatius</namePart>
<namePart type="family">Ezeani</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mo</namePart>
<namePart type="family">El-Haj</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yafei</namePart>
<namePart type="family">Zhu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yufeng</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-01</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 31st International Conference on Computational Linguistics: System Demonstrations</title>
</titleInfo>
<name type="personal">
<namePart type="given">Owen</namePart>
<namePart type="family">Rambow</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Leo</namePart>
<namePart type="family">Wanner</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marianna</namePart>
<namePart type="family">Apidianaki</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hend</namePart>
<namePart type="family">Al-Khalifa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Barbara</namePart>
<namePart type="given">Di</namePart>
<namePart type="family">Eugenio</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Steven</namePart>
<namePart type="family">Schockaert</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Brodie</namePart>
<namePart type="family">Mather</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mark</namePart>
<namePart type="family">Dras</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Abu Dhabi, UAE</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Learning entities from narratives of skin cancer (LENS) is an automatic entity recognition system built on colloquial writings from skin cancer-related Reddit forums. LENS encapsulates a comprehensive set of 24 labels that address clinical, demographic, and psychosocial aspects of skin cancer. Furthermore, we release LENS as a PyPI and pip package, making it easy for developers to download and install, and also provide a web application that allows users to get model predictions interactively, useful for researchers and individuals with minimal programming experience. Additionally, we publish the annotation guidelines designed specifically for spontaneous skin cancer narratives, that can be implemented to better understand and address challenges when developing corpora or systems for similar diseases. The model achieves an overall entity-level F1 score of 0.561, with notable performance for entities such as “CANC_T” (0.747), “STG” (0.788), “POB” (0.714), “GENDER” (0.750), “A/G” (0.714), and “PPL” (0.703). Other entities with significant results include “TRT” (0.625), “MED” (0.606), “AGE” (0.646), “EMO” (0.619), and “MHD” (0.5). We believe that LENS can serve as an essential tool supporting the analysis of patient discussions leading to improvements in the design and development of modern smart healthcare technologies.</abstract>
<identifier type="citekey">lal-etal-2025-lens</identifier>
<location>
<url>https://aclanthology.org/2025.coling-demos.3/</url>
</location>
<part>
<date>2025-01</date>
<extent unit="page">
<start>20</start>
<end>27</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T LENS: Learning Entities from Narratives of Skin Cancer
%A Lal, Daisy Monika
%A Rayson, Paul
%A Peter, Christopher
%A Ezeani, Ignatius
%A El-Haj, Mo
%A Zhu, Yafei
%A Liu, Yufeng
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%Y Mather, Brodie
%Y Dras, Mark
%S Proceedings of the 31st International Conference on Computational Linguistics: System Demonstrations
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F lal-etal-2025-lens
%X Learning entities from narratives of skin cancer (LENS) is an automatic entity recognition system built on colloquial writings from skin cancer-related Reddit forums. LENS encapsulates a comprehensive set of 24 labels that address clinical, demographic, and psychosocial aspects of skin cancer. Furthermore, we release LENS as a PyPI and pip package, making it easy for developers to download and install, and also provide a web application that allows users to get model predictions interactively, useful for researchers and individuals with minimal programming experience. Additionally, we publish the annotation guidelines designed specifically for spontaneous skin cancer narratives, that can be implemented to better understand and address challenges when developing corpora or systems for similar diseases. The model achieves an overall entity-level F1 score of 0.561, with notable performance for entities such as “CANC_T” (0.747), “STG” (0.788), “POB” (0.714), “GENDER” (0.750), “A/G” (0.714), and “PPL” (0.703). Other entities with significant results include “TRT” (0.625), “MED” (0.606), “AGE” (0.646), “EMO” (0.619), and “MHD” (0.5). We believe that LENS can serve as an essential tool supporting the analysis of patient discussions leading to improvements in the design and development of modern smart healthcare technologies.
%U https://aclanthology.org/2025.coling-demos.3/
%P 20-27
Markdown (Informal)
[LENS: Learning Entities from Narratives of Skin Cancer](https://aclanthology.org/2025.coling-demos.3/) (Lal et al., COLING 2025)
ACL
- Daisy Monika Lal, Paul Rayson, Christopher Peter, Ignatius Ezeani, Mo El-Haj, Yafei Zhu, and Yufeng Liu. 2025. LENS: Learning Entities from Narratives of Skin Cancer. In Proceedings of the 31st International Conference on Computational Linguistics: System Demonstrations, pages 20–27, Abu Dhabi, UAE. Association for Computational Linguistics.