International Workshop on Health Text Mining and Information Analysis最新文献

Curriculum-guided Abstractive Summarization for Mental Health Online Posts 以课程为导向的心理健康网络帖子摘要

International Workshop on Health Text Mining and Information Analysis Pub Date : 2023-02-02 DOI: 10.48550/arXiv.2302.00954

Sajad Sotudeh, Nazli Goharian, Hanieh Deilamsalehy, Franck Dernoncourt

{"title":"Curriculum-guided Abstractive Summarization for Mental Health Online Posts","authors":"Sajad Sotudeh, Nazli Goharian, Hanieh Deilamsalehy, Franck Dernoncourt","doi":"10.48550/arXiv.2302.00954","DOIUrl":"https://doi.org/10.48550/arXiv.2302.00954","url":null,"abstract":"Automatically generating short summaries from users’ online mental health posts could save counselors’ reading time and reduce their fatigue so that they can provide timely responses to those seeking help for improving their mental state. Recent Transformers-based summarization models have presented a promising approach to abstractive summarization. They go beyond sentence selection and extractive strategies to deal with more complicated tasks such as novel word generation and sentence paraphrasing. Nonetheless, these models have a prominent shortcoming; their training strategy is not quite efficient, which restricts the model’s performance. In this paper, we include a curriculum learning approach to reweigh the training samples, bringing about an efficient learning procedure. We apply our model on extreme summarization dataset of MentSum posts —-a dataset of mental health related posts from Reddit social media. Compared to the state-of-the-art model, our proposed method makes substantial gains in terms of Rouge and Bertscore evaluation metrics, yielding 3.5% Rouge-1, 10.4% Rouge-2, and 4.7% Rouge-L, 1.5% Bertscore relative improvements.","PeriodicalId":448872,"journal":{"name":"International Workshop on Health Text Mining and Information Analysis","volume":"236 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123191111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Proxy-based Zero-Shot Entity Linking by Effective Candidate Retrieval 基于有效候选检索的代理零射击实体链接

International Workshop on Health Text Mining and Information Analysis Pub Date : 2023-01-30 DOI: 10.48550/arXiv.2301.13318

Maciej Wiatrak, Eirini Arvaniti, Angus Brayne, Jonas Vetterle, Aaron Sim

引用次数: 1

The Impact of De-identification on Downstream Named Entity Recognition in Clinical Text 去识别对临床文本下游命名实体识别的影响

International Workshop on Health Text Mining and Information Analysis Pub Date : 2020-11-01 DOI: 10.18653/v1/2020.louhi-1.1

Hanna Berg, Aron Henriksson, H. Dalianis

{"title":"The Impact of De-identification on Downstream Named Entity Recognition in Clinical Text","authors":"Hanna Berg, Aron Henriksson, H. Dalianis","doi":"10.18653/v1/2020.louhi-1.1","DOIUrl":"https://doi.org/10.18653/v1/2020.louhi-1.1","url":null,"abstract":"The impact of de-identification on data quality and, in particular, utility for developing models for downstream tasks has been more thoroughly studied for structured data than for unstructured text. While previous studies indicate that text de-identification has a limited impact on models for downstream tasks, it remains unclear what the impact is with various levels and forms of de-identification, in particular concerning the trade-off between precision and recall. In this paper, the impact of de-identification is studied on downstream named entity recognition in Swedish clinical text. The results indicate that de-identification models with moderate to high precision lead to similar downstream performance, while low precision has a substantial negative impact. Furthermore, different strategies for concealing sensitive information affect performance to different degrees, ranging from pseudonymisation having a low impact to the removal of entire sentences with sensitive information having a high impact. This study indicates that it is possible to increase the recall of models for identifying sensitive information without negatively affecting the use of de-identified text data for training models for clinical named entity recognition; however, there is ultimately a trade-off between the level of de-identification and the subsequent utility of the data.","PeriodicalId":448872,"journal":{"name":"International Workshop on Health Text Mining and Information Analysis","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116644944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Detection of Mental Health from Reddit via Deep Contextualized Representations 基于深度情境化表征的Reddit用户心理健康检测

International Workshop on Health Text Mining and Information Analysis Pub Date : 2020-11-01 DOI: 10.18653/v1/2020.louhi-1.16

Zhengping Jiang, Sarah Ita Levitan, Jonathan Zomick, Julia Hirschberg

引用次数: 44

Simple Hierarchical Multi-Task Neural End-To-End Entity Linking for Biomedical Text 生物医学文本的简单分层多任务神经端到端实体链接

International Workshop on Health Text Mining and Information Analysis Pub Date : 2020-11-01 DOI: 10.18653/v1/2020.louhi-1.2

Maciej Wiatrak, Juha Iso-Sipilä

{"title":"Simple Hierarchical Multi-Task Neural End-To-End Entity Linking for Biomedical Text","authors":"Maciej Wiatrak, Juha Iso-Sipilä","doi":"10.18653/v1/2020.louhi-1.2","DOIUrl":"https://doi.org/10.18653/v1/2020.louhi-1.2","url":null,"abstract":"Recognising and linking entities is a crucial first step to many tasks in biomedical text analysis, such as relation extraction and target identification. Traditionally, biomedical entity linking methods rely heavily on heuristic rules and predefined, often domain-specific features. The features try to capture the properties of entities and complex multi-step architectures to detect, and subsequently link entity mentions. We propose a significant simplification to the biomedical entity linking setup that does not rely on any heuristic methods. The system performs all the steps of the entity linking task jointly in either single or two stages. We explore the use of hierarchical multi-task learning, using mention recognition and entity typing tasks as auxiliary tasks. We show that hierarchical multi-task models consistently outperform single-task models when trained tasks are homogeneous. We evaluate the performance of our models on the biomedical entity linking benchmarks using MedMentions and BC5CDR datasets. We achieve state-of-theart results on the challenging MedMentions dataset, and comparable results on BC5CDR.","PeriodicalId":448872,"journal":{"name":"International Workshop on Health Text Mining and Information Analysis","volume":"405 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123201563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Context-Aware Automatic Text Simplification of Health Materials in Low-Resource Domains 低资源域卫生资料的上下文感知自动文本简化

International Workshop on Health Text Mining and Information Analysis Pub Date : 2020-11-01 DOI: 10.18653/v1/2020.louhi-1.13

Tarek Sakakini, Jong Yoon Lee, Aditya Duri, R. F. Azevedo, V. Sadauskas, Kuangxiao Gu, S. Bhat, D. Morrow, J. Graumlich, Saqib Walayat, M. Hasegawa-Johnson, Thomas S. Huang, Ann M. Willemsen-Dunlap, Donald J. Halpin

{"title":"Context-Aware Automatic Text Simplification of Health Materials in Low-Resource Domains","authors":"Tarek Sakakini, Jong Yoon Lee, Aditya Duri, R. F. Azevedo, V. Sadauskas, Kuangxiao Gu, S. Bhat, D. Morrow, J. Graumlich, Saqib Walayat, M. Hasegawa-Johnson, Thomas S. Huang, Ann M. Willemsen-Dunlap, Donald J. Halpin","doi":"10.18653/v1/2020.louhi-1.13","DOIUrl":"https://doi.org/10.18653/v1/2020.louhi-1.13","url":null,"abstract":"Healthcare systems have increased patients’ exposure to their own health materials to enhance patients’ health levels, but this has been impeded by patients’ lack of understanding of their health material. We address potential barriers to their comprehension by developing a context-aware text simplification system for health material. Given the scarcity of annotated parallel corpora in healthcare domains, we design our system to be independent of a parallel corpus, complementing the availability of data-driven neural methods when such corpora are available. Our system compensates for the lack of direct supervision using a biomedical lexical database: Unified Medical Language System (UMLS). Compared to a competitive prior approach that uses a tool for identifying biomedical concepts and a consumer-directed vocabulary list, we empirically show the enhanced accuracy of our system due to improved handling of ambiguous terms. We also show the enhanced accuracy of our system over directly-supervised neural methods in this low-resource setting. Finally, we show the direct impact of our system on laypeople’s comprehension of health material via a human subjects’ study (n=160).","PeriodicalId":448872,"journal":{"name":"International Workshop on Health Text Mining and Information Analysis","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116418530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Multitask Learning of Negation and Speculation using Transformers 利用变形器进行多任务否定与思辨学习

International Workshop on Health Text Mining and Information Analysis Pub Date : 2020-11-01 DOI: 10.18653/v1/2020.louhi-1.9

Aditya P. Khandelwal, Benita Kathleen Britto

引用次数: 13

Information retrieval for animal disease surveillance: a pattern-based approach. 动物疾病监测的信息检索:基于模式的方法。

International Workshop on Health Text Mining and Information Analysis Pub Date : 2020-11-01 DOI: 10.18653/v1/2020.louhi-1.8

S. Valentin, R. Lancelot, M. Roche

引用次数: 0

Biomedical Event Extraction as Multi-turn Question Answering 基于多回合问答的生物医学事件提取

International Workshop on Health Text Mining and Information Analysis Pub Date : 2020-11-01 DOI: 10.18653/v1/2020.louhi-1.10

Xinglong Wang, Leon Weber, U. Leser

引用次数: 22

Not a cute stroke: Analysis of Rule- and Neural Network-based Information Extraction Systems for Brain Radiology Reports 不是一个可爱的中风:基于规则和神经网络的脑放射学报告信息提取系统分析

International Workshop on Health Text Mining and Information Analysis Pub Date : 2020-11-01 DOI: 10.18653/v1/2020.louhi-1.4

Andreas Grivas, Beatrice Alex, Claire Grover, R. Tobin, W. Whiteley

引用次数: 20