发布求助
文献互助 智能选刊 最新文献

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

筛选
英文 中文
Classification of Semantic Paraphasias: Optimization of a Word Embedding Model. 语义错述的分类:一个词嵌入模型的优化。
Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2019-06-01 DOI: 10.18653/v1/w19-2007
Katy McKinney-Bock, Steven Bedrick
{"title":"Classification of Semantic Paraphasias: Optimization of a Word Embedding Model.","authors":"Katy McKinney-Bock,&nbsp;Steven Bedrick","doi":"10.18653/v1/w19-2007","DOIUrl":"https://doi.org/10.18653/v1/w19-2007","url":null,"abstract":"<p><p>In clinical assessment of people with aphasia, impairment in the ability to recall and produce words for objects (<i>anomia</i>) is assessed using a confrontation naming task, where a target stimulus is viewed and a corresponding label is spoken by the participant. Vector space word embedding models have had inital results in assessing semantic similarity of target-production pairs in order to automate scoring of this task; however, the resulting models are also highly dependent upon training parameters. To select an optimal family of models, we fit a beta regression model to the distribution of performance metrics on a set of 2,880 grid search models and evaluate the resultant first- and second-order effects to explore how parameterization affects model performance. Comparing to SimLex-999, we show that clinical data can be used in an evaluation task with comparable optimal parameter settings as standard NLP evaluation datasets.</p>","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":"2019 RepEval","pages":"52-62"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10328545/pdf/nihms-1908531.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9808366","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: 3
Extracting Adverse Drug Event Information with Minimal Engineering. 基于最小工程的药物不良事件信息提取。
Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2019-06-01 DOI: 10.18653/v1/w19-1903
Timothy Miller, Alon Geva, Dmitriy Dligach
{"title":"Extracting Adverse Drug Event Information with Minimal Engineering.","authors":"Timothy Miller,&nbsp;Alon Geva,&nbsp;Dmitriy Dligach","doi":"10.18653/v1/w19-1903","DOIUrl":"https://doi.org/10.18653/v1/w19-1903","url":null,"abstract":"<p><p>In this paper we describe an evaluation of the potential of classical information extraction methods to extract drug-related attributes, including adverse drug events, and compare to more recently developed neural methods. We use the 2018 N2C2 shared task data as our gold standard data set for training. We train support vector machine classifiers to detect drug and drug attribute spans, and pair these detected entities as training instances for an SVM relation classifier, with both systems using standard features. We compare to baseline neural methods that use standard contextualized embedding representations for entity and relation extraction. The SVM-based system and a neural system obtain comparable results, with the SVM system doing better on concepts and the neural system performing better on relation extraction tasks. The neural system obtains surprisingly strong results compared to the system based on years of research in developing features for information extraction.</p>","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":"2019 ","pages":"22-27"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8140592/pdf/nihms-1035507.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39012326","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: 10
Simplified Neural Unsupervised Domain Adaptation 简化神经无监督域自适应
Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2019-05-22 DOI: 10.18653/v1/N19-1039
Timothy Miller
{"title":"Simplified Neural Unsupervised Domain Adaptation","authors":"Timothy Miller","doi":"10.18653/v1/N19-1039","DOIUrl":"https://doi.org/10.18653/v1/N19-1039","url":null,"abstract":"Unsupervised domain adaptation (UDA) is the task of training a statistical model on labeled data from a source domain to achieve better performance on data from a target domain, with access to only unlabeled data in the target domain. Existing state-of-the-art UDA approaches use neural networks to learn representations that are trained to predict the values of subset of important features called “pivot features” on combined data from the source and target domains. In this work, we show that it is possible to improve on existing neural domain adaptation algorithms by 1) jointly training the representation learner with the task learner; and 2) removing the need for heuristically-selected “pivot features.” Our results show competitive performance with a simpler model.","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":"1 1","pages":"414-419"},"PeriodicalIF":0.0,"publicationDate":"2019-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72895171","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: 24
Oral-Motor and Lexical Diversity During Naturalistic Conversations in Adults with Autism Spectrum Disorder. 自闭症谱系障碍成人在自然对话中的口语运动和词汇多样性。
Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2018-06-01 DOI: 10.18653/v1/w18-0616
Julia Parish-Morris, Evangelos Sariyanidi, Casey Zampella, G Keith Bartley, Emily Ferguson, Ashley A Pallathra, Leila Bateman, Samantha Plate, Meredith Cola, Juhi Pandey, Edward S Brodkin, Robert T Schultz, Birkan Tunç
{"title":"Oral-Motor and Lexical Diversity During Naturalistic Conversations in Adults with Autism Spectrum Disorder.","authors":"Julia Parish-Morris, Evangelos Sariyanidi, Casey Zampella, G Keith Bartley, Emily Ferguson, Ashley A Pallathra, Leila Bateman, Samantha Plate, Meredith Cola, Juhi Pandey, Edward S Brodkin, Robert T Schultz, Birkan Tunç","doi":"10.18653/v1/w18-0616","DOIUrl":"10.18653/v1/w18-0616","url":null,"abstract":"<p><p>Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by impaired social communication and the presence of restricted, repetitive patterns of behaviors and interests. Prior research suggests that restricted patterns of behavior in ASD may be cross-domain phenomena that are evident in a variety of modalities. Computational studies of language in ASD provide support for the existence of an underlying dimension of restriction that emerges during a conversation. Similar evidence exists for restricted patterns of facial movement. Using tools from computational linguistics, computer vision, and information theory, this study tests whether cognitive-motor restriction can be detected across multiple behavioral domains in adults with ASD during a naturalistic conversation. Our methods identify restricted behavioral patterns, as measured by entropy in word use and mouth movement. Results suggest that adults with ASD produce significantly less diverse mouth movements and words than neurotypical adults, with an increased reliance on repeated patterns in both domains. The diversity values of the two domains are not significantly correlated, suggesting that they provide complementary information.</p>","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":"2018 ","pages":"147-157"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7558464/pdf/nihms-985188.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38502652","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
Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos. 二元对话视频中情感识别的会话记忆网络。
Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2018-06-01 DOI: 10.18653/v1/n18-1193
Devamanyu Hazarika, Soujanya Poria, Amir Zadeh, Erik Cambria, Louis-Philippe Morency, Roger Zimmermann
{"title":"Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos.","authors":"Devamanyu Hazarika,&nbsp;Soujanya Poria,&nbsp;Amir Zadeh,&nbsp;Erik Cambria,&nbsp;Louis-Philippe Morency,&nbsp;Roger Zimmermann","doi":"10.18653/v1/n18-1193","DOIUrl":"https://doi.org/10.18653/v1/n18-1193","url":null,"abstract":"<p><p>Emotion recognition in conversations is crucial for the development of empathetic machines. Present methods mostly ignore the role of inter-speaker dependency relations while classifying emotions in conversations. In this paper, we address recognizing utterance-level emotions in dyadic conversational videos. We propose a deep neural framework, termed conversational memory network, which leverages contextual information from the conversation history. The framework takes a multimodal approach comprising audio, visual and textual features with gated recurrent units to model past utterances of each speaker into memories. Such memories are then merged using attention-based hops to capture inter-speaker dependencies. Experiments show an accuracy improvement of 3-4% over the state of the art.</p>","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":"2018 ","pages":"2122-2132"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.18653/v1/n18-1193","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37778199","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: 282
Syntactic Patterns Improve Information Extraction for Medical Search. 语法模式改进医学搜索的信息提取。
Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2018-06-01
Roma Patel, Yinfei Yang, Iain Marshall, Ani Nenkova, Byron C Wallace
{"title":"Syntactic Patterns Improve Information Extraction for Medical Search.","authors":"Roma Patel, Yinfei Yang, Iain Marshall, Ani Nenkova, Byron C Wallace","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Medical professionals search the published literature by specifying the type of <i>patients</i>, the medical <i>intervention(s)</i> and the <i>outcome</i> measure(s) of interest. In this paper we demonstrate how features encoding syntactic patterns improve the performance of state-of-the-art sequence tagging models (both linear and neural) for information extraction of these medically relevant categories. We present an analysis of the type of patterns exploited, and the semantic space induced for these, i.e., the distributed representations learned for identified multi-token patterns. We show that these learned representations differ substantially from those of the constituent unigrams, suggesting that the patterns capture contextual information that is otherwise lost.</p>","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":"2018 Short Paper","pages":"371-377"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6174535/pdf/nihms-988061.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36563083","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
EMR Coding with Semi-Parametric Multi-Head Matching Networks. 半参数多头匹配网络的EMR编码。
Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2018-06-01 DOI: 10.18653/v1/N18-1189
Anthony Rios, Ramakanth Kavuluru
{"title":"EMR Coding with Semi-Parametric Multi-Head Matching Networks.","authors":"Anthony Rios,&nbsp;Ramakanth Kavuluru","doi":"10.18653/v1/N18-1189","DOIUrl":"https://doi.org/10.18653/v1/N18-1189","url":null,"abstract":"<p><p>Coding EMRs with diagnosis and procedure codes is an indispensable task for billing, secondary data analyses, and monitoring health trends. Both speed and accuracy of coding are critical. While coding errors could lead to more patient-side financial burden and mis-interpretation of a patient's well-being, timely coding is also needed to avoid backlogs and additional costs for the healthcare facility. In this paper, we present a new neural network architecture that combines ideas from few-shot learning matching networks, multi-label loss functions, and convolutional neural networks for text classification to significantly outperform other state-of-the-art models. Our evaluations are conducted using a well known deidentified EMR dataset (MIMIC) with a variety of multi-label performance measures.</p>","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":"2018 ","pages":"2081-2091"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6105925/pdf/nihms-985153.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36432294","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: 23
A Multi-Context Character Prediction Model for a Brain-Computer Interface. 脑机接口的多上下文字符预测模型。
Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2018-06-01 DOI: 10.18653/v1/w18-1210
Shiran Dudy, Steven Bedrick, Shaobin Xu, David A Smith
{"title":"A Multi-Context Character Prediction Model for a Brain-Computer Interface.","authors":"Shiran Dudy,&nbsp;Steven Bedrick,&nbsp;Shaobin Xu,&nbsp;David A Smith","doi":"10.18653/v1/w18-1210","DOIUrl":"https://doi.org/10.18653/v1/w18-1210","url":null,"abstract":"<p><p>Brain-computer interfaces and other augmentative and alternative communication devices introduce language-modeing challenges distinct from other character-entry methods. In particular, the acquired signal of the EEG (electroencephalogram) signal is noisier, which, in turn, makes the user intent harder to decipher. In order to adapt to this condition, we propose to maintain ambiguous history for every time step, and to employ, apart from the character language model, word information to produce a more robust prediction system. We present preliminary results that compare this proposed Online-Context Language Model (OCLM) to current algorithms that are used in this type of setting. Evaluations on both perplexity and predictive accuracy demonstrate promising results when dealing with ambiguous histories in order to provide to the front end a distribution of the next character the user might type.</p>","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":"2018 ","pages":"72-77"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8087439/pdf/nihms-1001613.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38861816","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: 3
Syntactic Patterns Improve Information Extraction for Medical Search 语法模式改进医学搜索的信息提取
Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2018-04-01 DOI: 10.18653/v1/N18-2060
Roma Patel, Yinfei Yang, I. Marshall, A. Nenkova, Byron C. Wallace
{"title":"Syntactic Patterns Improve Information Extraction for Medical Search","authors":"Roma Patel, Yinfei Yang, I. Marshall, A. Nenkova, Byron C. Wallace","doi":"10.18653/v1/N18-2060","DOIUrl":"https://doi.org/10.18653/v1/N18-2060","url":null,"abstract":"Medical professionals search the published literature by specifying the type of patients, the medical intervention(s) and the outcome measure(s) of interest. In this paper we demonstrate how features encoding syntactic patterns improve the performance of state-of-the-art sequence tagging models (both neural and linear) for information extraction of these medically relevant categories. We present an analysis of the type of patterns exploited and of the semantic space induced for these, i.e., the distributed representations learned for identified multi-token patterns. We show that these learned representations differ substantially from those of the constituent unigrams, suggesting that the patterns capture contextual information that is otherwise lost.","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":"18 1","pages":"371-377"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74621974","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: 12
Bidirectional RNN for Medical Event Detection in Electronic Health Records 基于双向RNN的电子病历医疗事件检测
Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2016-06-01 DOI: 10.18653/v1/N16-1056
Abhyuday N. Jagannatha, Hong Yu
{"title":"Bidirectional RNN for Medical Event Detection in Electronic Health Records","authors":"Abhyuday N. Jagannatha, Hong Yu","doi":"10.18653/v1/N16-1056","DOIUrl":"https://doi.org/10.18653/v1/N16-1056","url":null,"abstract":"Sequence labeling for extraction of medical events and their attributes from unstructured text in Electronic Health Record (EHR) notes is a key step towards semantic understanding of EHRs. It has important applications in health informatics including pharmacovigilance and drug surveillance. The state of the art supervised machine learning models in this domain are based on Conditional Random Fields (CRFs) with features calculated from fixed context windows. In this application, we explored recurrent neural network frameworks and show that they significantly out-performed the CRF models.","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":"40 1","pages":"473-482"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82718166","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: 271
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信