Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing最新文献

A Comprehensive Evaluation of Biomedical Entity Linking Models. 生物医学实体链接模型的综合评估。

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing Pub Date : 2023-12-01 DOI: 10.18653/v1/2023.emnlp-main.893

David Kartchner, Jennifer Deng, Shubham Lohiya, Tejasri Kopparthi, Prasanth Bathala, Daniel Domingo-Fernández, Cassie S Mitchell

{"title":"A Comprehensive Evaluation of Biomedical Entity Linking Models.","authors":"David Kartchner, Jennifer Deng, Shubham Lohiya, Tejasri Kopparthi, Prasanth Bathala, Daniel Domingo-Fernández, Cassie S Mitchell","doi":"10.18653/v1/2023.emnlp-main.893","DOIUrl":"https://doi.org/10.18653/v1/2023.emnlp-main.893","url":null,"abstract":"Biomedical entity linking (BioEL) is the process of connecting entities referenced in documents to entries in biomedical databases such as the Unified Medical Language System (UMLS) or Medical Subject Headings (MeSH). The study objective was to comprehensively evaluate nine recent state-of-the-art biomedical entity linking models under a unified framework. We compare these models along axes of (1) accuracy, (2) speed, (3) ease of use, (4) generalization, and (5) adaptability to new ontologies and datasets. We additionally quantify the impact of various preprocessing choices such as abbreviation detection. Systematic evaluation reveals several notable gaps in current methods. In particular, current methods struggle to correctly link genes and proteins and often have difficulty effectively incorporating context into linking decisions. To expedite future development and baseline testing, we release our unified evaluation framework and all included models on GitHub at https://github.com/davidkartchner/biomedical-entity-linking.","PeriodicalId":74540,"journal":{"name":"Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing","volume":"2023 ","pages":"14462-14478"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11097978/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140961102","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

Hierarchical Pretraining on Multimodal Electronic Health Records. 多模态电子健康记录的分层预培训。

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing Pub Date : 2023-12-01 DOI: 10.18653/v1/2023.emnlp-main.171

Xiaochen Wang, Junyu Luo, Jiaqi Wang, Ziyi Yin, Suhan Cui, Yuan Zhong, Yaqing Wang, Fenglong Ma

引用次数: 0

Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Data. 使用大型语言模型生成临床数据的两个方向：数据到标签（Data-to-Label）和标签到数据（Label-to-Data）。

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing Pub Date : 2023-12-01 DOI: 10.18653/v1/2023.findings-emnlp.474

Rumeng Li, Xun Wang, Hong Yu

{"title":"Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Data.","authors":"Rumeng Li, Xun Wang, Hong Yu","doi":"10.18653/v1/2023.findings-emnlp.474","DOIUrl":"10.18653/v1/2023.findings-emnlp.474","url":null,"abstract":"Large language models (LLMs) can generate natural language texts for various domains and tasks, but their potential for clinical text mining, a domain with scarce, sensitive, and imbalanced medical data, is under-explored. We investigate whether LLMs can augment clinical data for detecting Alzheimer's Disease (AD)-related signs and symptoms from electronic health records (EHRs), a challenging task that requires high expertise. We create a novel pragmatic taxonomy for AD sign and symptom progression based on expert knowledge and generated three datasets: (1) a gold dataset annotated by human experts on longitudinal EHRs of AD patients; (2) a silver dataset created by the data-to-label method, which labels sentences from a public EHR collection with AD-related signs and symptoms; and (3) a bronze dataset created by the label-to-data method which generates sentences with AD-related signs and symptoms based on the label definition. We train a system to detect AD-related signs and symptoms from EHRs. We find that the silver and bronze datasets improves the system performance, outperforming the system using only the gold dataset. This shows that LLMs can generate synthetic clinical data for a complex task by incorporating expert knowledge, and our label-to-data method can produce datasets that are free of sensitive information, while maintaining acceptable quality.","PeriodicalId":74540,"journal":{"name":"Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing","volume":"2023 ","pages":"7129-7143"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10782150/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139426222","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

An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives. 心理健康对话代理综合调查，架起计算机科学与医学视角的桥梁。

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing Pub Date : 2023-12-01 DOI: 10.18653/v1/2023.emnlp-main.698

Young-Min Cho, Sunny Rai, Lyle Ungar, João Sedoc, Sharath Chandra Guntuku

{"title":"An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives.","authors":"Young-Min Cho, Sunny Rai, Lyle Ungar, João Sedoc, Sharath Chandra Guntuku","doi":"10.18653/v1/2023.emnlp-main.698","DOIUrl":"https://doi.org/10.18653/v1/2023.emnlp-main.698","url":null,"abstract":"Mental health conversational agents (a.k.a. chatbots) are widely studied for their potential to offer accessible support to those experiencing mental health challenges. Previous surveys on the topic primarily consider papers published in either computer science or medicine, leading to a divide in understanding and hindering the sharing of beneficial knowledge between both domains. To bridge this gap, we conduct a comprehensive literature review using the PRISMA framework, reviewing 534 papers published in both computer science and medicine. Our systematic review reveals 136 key papers on building mental health-related conversational agents with diverse characteristics of modeling and experimental design techniques. We find that computer science papers focus on LLM techniques and evaluating response quality using automated metrics with little attention to the application while medical papers use rule-based conversational agents and outcome metrics to measure the health outcomes of participants. Based on our findings on transparency, ethics, and cultural heterogeneity in this review, we provide a few recommendations to help bridge the disciplinary divide and enable the cross-disciplinary development of mental health conversational agents.","PeriodicalId":74540,"journal":{"name":"Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing","volume":"2023 ","pages":"11346-11369"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11010238/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140874091","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

Sentence-Incremental Neural Coreference Resolution 句子-增量神经关联解析

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing Pub Date : 2023-05-26 DOI: 10.48550/arXiv.2305.16947

Matt Grenander, Shay B. Cohen, Mark Steedman

{"title":"Sentence-Incremental Neural Coreference Resolution","authors":"Matt Grenander, Shay B. Cohen, Mark Steedman","doi":"10.48550/arXiv.2305.16947","DOIUrl":"https://doi.org/10.48550/arXiv.2305.16947","url":null,"abstract":"We propose a sentence-incremental neural coreference resolution system which incrementally builds clusters after marking mention boundaries in a shift-reduce method. The system is aimed at bridging two recent approaches at coreference resolution: (1) state-of-the-art non-incremental models that incur quadratic complexity in document length with high computational cost, and (2) memory network-based models which operate incrementally but do not generalize beyond pronouns. For comparison, we simulate an incremental setting by constraining non-incremental systems to form partial coreference chains before observing new sentences. In this setting, our system outperforms comparable state-of-the-art methods by 2 F1 on OntoNotes and 6.8 F1 on the CODI-CRAC 2021 corpus. In a conventional coreference setup, our system achieves 76.3 F1 on OntoNotes and 45.5 F1 on CODI-CRAC 2021, which is comparable to state-of-the-art baselines. We also analyze variations of our system and show that the degree of incrementality in the encoder has a surprisingly large effect on the resulting performance.","PeriodicalId":74540,"journal":{"name":"Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing","volume":"46 1","pages":"427-443"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78534266","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: 3

Self-Distillation with Meta Learning for Knowledge Graph Completion 基于元学习的知识图谱补全自蒸馏

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing Pub Date : 2023-05-20 DOI: 10.48550/arXiv.2305.12209

Yunshui Li, Junhao Liu, Min Yang, Chengming Li

{"title":"Self-Distillation with Meta Learning for Knowledge Graph Completion","authors":"Yunshui Li, Junhao Liu, Min Yang, Chengming Li","doi":"10.48550/arXiv.2305.12209","DOIUrl":"https://doi.org/10.48550/arXiv.2305.12209","url":null,"abstract":"In this paper, we propose a selfdistillation framework with meta learning(MetaSD) for knowledge graph completion with dynamic pruning, which aims to learn compressed graph embeddings and tackle the longtail samples. Specifically, we first propose a dynamic pruning technique to obtain a small pruned model from a large source model, where the pruning mask of the pruned model could be updated adaptively per epoch after the model weights are updated. The pruned model is supposed to be more sensitive to difficult to memorize samples(e.g., longtail samples) than the source model. Then, we propose a onestep meta selfdistillation method for distilling comprehensive knowledge from the source model to the pruned model, where the two models coevolve in a dynamic manner during training. In particular, we exploit the performance of the pruned model, which is trained alongside the source model in one iteration, to improve the source models knowledge transfer ability for the next iteration via meta learning. Extensive experiments show that MetaSD achieves competitive performance compared to strong baselines, while being 10x smaller than baselines.","PeriodicalId":74540,"journal":{"name":"Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing","volume":"28 1","pages":"2048-2054"},"PeriodicalIF":0.0,"publicationDate":"2023-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89290748","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: 1

Learn What Is Possible, Then Choose What Is Best: Disentangling One-To-Many Relations in Language Through Text-based Games 学习什么是可能的，然后选择什么是最好的:通过基于文本的游戏解开一对多的语言关系

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing Pub Date : 2023-04-14 DOI: 10.48550/arXiv.2304.07258

Benjamin Towle, Ke Zhou

{"title":"Learn What Is Possible, Then Choose What Is Best: Disentangling One-To-Many Relations in Language Through Text-based Games","authors":"Benjamin Towle, Ke Zhou","doi":"10.48550/arXiv.2304.07258","DOIUrl":"https://doi.org/10.48550/arXiv.2304.07258","url":null,"abstract":"Language models pre-trained on large self-supervised corpora, followed by task-specific fine-tuning has become the dominant paradigm in NLP. These pre-training datasets often have a one-to-many structure--e.g. in dialogue there are many valid responses for a given context. However, only some of these responses will be desirable in our downstream task. This raises the question of how we should train the model such that it can emulate the desirable behaviours, but not the undesirable ones. Current approaches train in a one-to-one setup--only a single target response is given for a single dialogue context--leading to models only learning to predict the average response, while ignoring the full range of possible responses. Using text-based games as a testbed, our approach, PASA, uses discrete latent variables to capture the range of different behaviours represented in our larger pre-training dataset. We then use knowledge distillation to distil the posterior probability distribution into a student model. This probability distribution is far richer than learning from only the hard targets of the dataset, and thus allows the student model to benefit from the richer range of actions the teacher model has learned. Results show up to 49% empirical improvement over the previous state-of-the-art model on the Jericho Walkthroughs dataset.","PeriodicalId":74540,"journal":{"name":"Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing","volume":"60 1","pages":"4955-4965"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75086365","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: 1

Empowering Dual-Encoder with Query Generator for Cross-Lingual Dense Retrieval 用查询生成器增强双编码器跨语言密集检索能力

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing Pub Date : 2023-03-27 DOI: 10.48550/arXiv.2303.14991

Houxing Ren, Linjun Shou, Ning Wu, Ming Gong, Daxin Jiang

引用次数: 1

Borrowing Human Senses: Comment-Aware Self-Training for Social Media Multimodal Classification 借用人类感官:社交媒体多模态分类的评论感知自我训练

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing Pub Date : 2023-03-27 DOI: 10.48550/arXiv.2303.15016

Chunpu Xu, Jing Li

引用次数: 2

Understanding Social Media Cross-Modality Discourse in Linguistic Space 从语言空间看社交媒体跨情态话语

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing Pub Date : 2023-02-26 DOI: 10.48550/arXiv.2302.13311

Chunpu Xu, Hanzhuo Tan, Jing Li, Piji Li

引用次数: 1