Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting最新文献

ODD: A Benchmark Dataset for the Natural Language Processing Based Opioid Related Aberrant Behavior Detection. ODD：基于自然语言处理的阿片类药物相关异常行为检测基准数据集。

Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2024-06-01

Sunjae Kwon, Xun Wang, Weisong Liu, Emily Druhl, Minhee L Sung, Joel I Reisman, Wenjun Li, Robert D Kerns, William Becker, Hong Yu

{"title":"ODD: A Benchmark Dataset for the Natural Language Processing Based Opioid Related Aberrant Behavior Detection.","authors":"Sunjae Kwon, Xun Wang, Weisong Liu, Emily Druhl, Minhee L Sung, Joel I Reisman, Wenjun Li, Robert D Kerns, William Becker, Hong Yu","doi":"","DOIUrl":"","url":null,"abstract":"Opioid related aberrant behaviors (ORABs) present novel risk factors for opioid overdose. This paper introduces a novel biomedical natural language processing benchmark dataset named ODD, for ORAB Detection Dataset. ODD is an expert-annotated dataset designed to identify ORABs from patients' EHR notes and classify them into nine categories; 1) Confirmed Aberrant Behavior, 2) Suggested Aberrant Behavior, 3) Opioids, 4) Indication, 5) Diagnosed opioid dependency, 6) Benzodiazepines, 7) Medication Changes, 8) Central Nervous System-related, and 9) Social Determinants of Health. We explored two state-of-the-art natural language processing models (fine-tuning and prompt-tuning approaches) to identify ORAB. Experimental results show that the prompt-tuning models outperformed the fine-tuning models in most categories and the gains were especially higher among uncommon categories (Suggested Aberrant Behavior, Confirmed Aberrant Behaviors, Diagnosed Opioid Dependence, and Medication Change). Although the best model achieved the highest 88.17% on macro average area under precision recall curve, uncommon classes still have a large room for performance improvement. ODD is publicly available.","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11368170/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142121292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ScAN: Suicide Attempt and Ideation Events Dataset. 扫描:自杀企图和构思事件数据集。

Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2022-07-01 DOI: 10.18653/v1/2022.naacl-main.75

Bhanu Pratap Singh Rawat, Samuel Kovaly, Wilfred R Pigeon, Hong Yu

{"title":"ScAN: Suicide Attempt and Ideation Events Dataset.","authors":"Bhanu Pratap Singh Rawat, Samuel Kovaly, Wilfred R Pigeon, Hong Yu","doi":"10.18653/v1/2022.naacl-main.75","DOIUrl":"https://doi.org/10.18653/v1/2022.naacl-main.75","url":null,"abstract":"Suicide is an important public health concern and one of the leading causes of death worldwide. Suicidal behaviors, including suicide attempts (SA) and suicide ideations (SI), are leading risk factors for death by suicide. Information related to patients' previous and current SA and SI are frequently documented in the electronic health record (EHR) notes. Accurate detection of such documentation may help improve surveillance and predictions of patients' suicidal behaviors and alert medical professionals for suicide prevention efforts. In this study, we first built Suicide Attempt and Ideation Events (ScAN) dataset, a subset of the publicly available MIMIC III dataset spanning over 12k+ EHR notes with 19k+ annotated SA and SI events information. The annotations also contain attributes such as method of suicide attempt. We also provide a strong baseline model ScANER (Suicide Attempt and Ideation Events Retreiver), a multi-task RoBERTa-based model with a retrieval module to extract all the relevant suicidal behavioral evidences from EHR notes of an hospital-stay and, and a prediction module to identify the type of suicidal behavior (SA and SI) concluded during the patient's stay at the hospital. ScANER achieved a macro-weighted F1-score of 0.83 for identifying suicidal behavioral evidences and a macro F1-score of 0.78 and 0.60 for classification of SA and SI for the patient's hospital-stay, respectively. ScAN and ScANER are publicly available.","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9958515/pdf/nihms-1875183.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9423903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ScAN: Suicide Attempt and Ideation Events Dataset 扫描:自杀企图和构思事件数据集

Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2022-05-12 DOI: 10.48550/arXiv.2205.07872

Bhanu Pratap Singh Rawat, Samuel Kovaly, W. Pigeon, Hong-ye Yu

{"title":"ScAN: Suicide Attempt and Ideation Events Dataset","authors":"Bhanu Pratap Singh Rawat, Samuel Kovaly, W. Pigeon, Hong-ye Yu","doi":"10.48550/arXiv.2205.07872","DOIUrl":"https://doi.org/10.48550/arXiv.2205.07872","url":null,"abstract":"Suicide is an important public health concern and one of the leading causes of death worldwide. Suicidal behaviors, including suicide attempts (SA) and suicide ideations (SI), are leading risk factors for death by suicide. Information related to patients’ previous and current SA and SI are frequently documented in the electronic health record (EHR) notes. Accurate detection of such documentation may help improve surveillance and predictions of patients’ suicidal behaviors and alert medical professionals for suicide prevention efforts. In this study, we first built Suicide Attempt and Ideation Events (ScAN) dataset, a subset of the publicly available MIMIC III dataset spanning over 12k+ EHR notes with 19k+ annotated SA and SI events information. The annotations also contain attributes such as method of suicide attempt. We also provide a strong baseline model ScANER (Suicide Attempt and Ideation Events Retriever), a multi-task RoBERTa-based model with a retrieval module to extract all the relevant suicidal behavioral evidences from EHR notes of an hospital-stay and, and a prediction module to identify the type of suicidal behavior (SA and SI) concluded during the patient’s stay at the hospital. ScANER achieved a macro-weighted F1-score of 0.83 for identifying suicidal behavioral evidences and a macro F1-score of 0.78 and 0.60 for classification of SA and SI for the patient’s hospital-stay, respectively. ScAN and ScANER are publicly available.","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78256254","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: 4

Analysis of Behavior Classification in Motivational Interviewing. 动机访谈中的行为分类分析。

Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2021-06-01 DOI: 10.18653/v1/2021.clpsych-1.13

Leili Tavabi, Trang Tran, Kalin Stefanov, Brian Borsari, Joshua D Woolley, Stefan Scherer, Mohammad Soleymani

{"title":"Analysis of Behavior Classification in Motivational Interviewing.","authors":"Leili Tavabi, Trang Tran, Kalin Stefanov, Brian Borsari, Joshua D Woolley, Stefan Scherer, Mohammad Soleymani","doi":"10.18653/v1/2021.clpsych-1.13","DOIUrl":"10.18653/v1/2021.clpsych-1.13","url":null,"abstract":"Analysis of client and therapist behavior in counseling sessions can provide helpful insights for assessing the quality of the session and consequently, the client's behavioral outcome. In this paper, we study the automatic classification of standardized behavior codes (i.e. annotations) used for assessment of psychotherapy sessions in Motivational Interviewing (MI). We develop models and examine the classification of client behaviors throughout MI sessions, comparing the performance by models trained on large pretrained embeddings (RoBERTa) versus interpretable and expert-selected features (LIWC). Our best performing model using the pretrained RoBERTa embeddings beats the baseline model, achieving an F1 score of 0.66 in the subject-independent 3-class classification. Through statistical analysis on the classification results, we identify prominent LIWC features that may not have been captured by the model using pretrained embeddings. Although classification using LIWC features underperforms RoBERTa, our findings motivate the future direction of incorporating auxiliary tasks in the classification of MI codes.","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321779/pdf/nihms-1727153.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39266882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TextEssence: A Tool for Interactive Analysis of Semantic Shifts Between Corpora. TextEssence:语料库间语义转换交互分析工具。

Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2021-06-01

Denis Newman-Griffis, Venkatesh Sivaraman, Adam Perer, Eric Fosler-Lussier, Harry Hochheiser

引用次数: 0

Empirical Evaluation of Pre-trained Transformers for Human-Level NLP: The Role of Sample Size and Dimensionality. 对人类水平NLP的预训练变压器的经验评价:样本大小和维度的作用。

Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2021-06-01 DOI: 10.18653/v1/2021.naacl-main.357

Adithya V Ganesan, Matthew Matero, Aravind Reddy Ravula, Huy Vu, H Andrew Schwartz

{"title":"Empirical Evaluation of Pre-trained Transformers for Human-Level NLP: The Role of Sample Size and Dimensionality.","authors":"Adithya V Ganesan, Matthew Matero, Aravind Reddy Ravula, Huy Vu, H Andrew Schwartz","doi":"10.18653/v1/2021.naacl-main.357","DOIUrl":"https://doi.org/10.18653/v1/2021.naacl-main.357","url":null,"abstract":"In human-level NLP tasks, such as predicting mental health, personality, or demographics, the number of observations is often smaller than the standard 768+ hidden state sizes of each layer within modern transformer-based language models, limiting the ability to effectively leverage transformers. Here, we provide a systematic study on the role of dimension reduction methods (principal components analysis, factorization techniques, or multi-layer auto-encoders) as well as the dimensionality of embedding vectors and sample sizes as a function of predictive performance. We first find that fine-tuning large models with a limited amount of data pose a significant difficulty which can be overcome with a pre-trained dimension reduction regime. RoBERTa consistently achieves top performance in human-level tasks, with PCA giving benefit over other reduction methods in better handling users that write longer texts. Finally, we observe that a majority of the tasks achieve results comparable to the best performance with just <math> <mrow><mfrac><mn>1</mn> <mrow><mn>12</mn></mrow> </mfrac> </mrow> </math> of the embedding dimensions.","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8294338/pdf/nihms-1716243.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39215546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

Translational NLP: A New Paradigm and General Principles for Natural Language Processing Research. 翻译NLP:自然语言处理研究的新范式和一般原则。

Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2021-06-01

Denis Newman-Griffis, Jill Fain Lehman, Carolyn Rosé, Harry Hochheiser

{"title":"Translational NLP: A New Paradigm and General Principles for Natural Language Processing Research.","authors":"Denis Newman-Griffis, Jill Fain Lehman, Carolyn Rosé, Harry Hochheiser","doi":"","DOIUrl":"","url":null,"abstract":"Natural language processing (NLP) research combines the study of universal principles, through basic science, with applied science targeting specific use cases and settings. However, the process of exchange between basic NLP and applications is often assumed to emerge naturally, resulting in many innovations going unapplied and many important questions left unstudied. We describe a new paradigm of Translational NLP, which aims to structure and facilitate the processes by which basic and applied NLP research inform one another. Translational NLP thus presents a third research paradigm, focused on understanding the challenges posed by application needs and how these challenges can drive innovation in basic science and technology design. We show that many significant advances in NLP research have emerged from the intersection of basic principles with application needs, and present a conceptual framework outlining the stakeholders and key questions in translational research. Our framework provides a roadmap for developing Translational NLP as a dedicated research area, and identifies general translational principles to facilitate exchange between basic and applied research.","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8223521/pdf/nihms-1710048.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39115253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging Deep Representations of Radiology Reports in Survival Analysis for Predicting Heart Failure Patient Mortality. 在生存分析中利用放射学报告的深度表征预测心衰患者的死亡率。

Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2021-06-01 DOI: 10.18653/v1/2021.naacl-main.358

Hyun Gi Lee, Evan Sholle, Ashley Beecy, Subhi Al'Aref, Yifan Peng

引用次数: 0

What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization. 摘要有哪些内容？为医院课程总结的进步奠定基础。

Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2021-06-01 DOI: 10.18653/v1/2021.naacl-main.382

Griffin Adams, Emily Alsentzer, Mert Ketenci, Jason Zucker, Noémie Elhadad

引用次数: 0

Word centrality constrained representation for keyphrase extraction. 关键词提取的词中心性约束表示。

Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2021-06-01 DOI: 10.18653/v1/2021.bionlp-1.17

Zelalem Gero, Joyce C Ho

引用次数: 5