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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,&nbsp;Yinfei Yang,&nbsp;Iain Marshall,&nbsp;Ani Nenkova,&nbsp;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
Similarity Measures for Quantifying Restrictive and Repetitive Behavior in Conversations of Autistic Children. 孤独症儿童对话中限制性和重复性行为量化的相似性测量。
Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2015-06-05
Masoud Rouhizadeh, Richard Sproat, Jan van Santen
{"title":"Similarity Measures for Quantifying Restrictive and Repetitive Behavior in Conversations of Autistic Children.","authors":"Masoud Rouhizadeh,&nbsp;Richard Sproat,&nbsp;Jan van Santen","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Restrictive and repetitive behavior (RRB) is a core symptom of autism spectrum disorder (ASD) and are manifest in language. Based on this, we expect children with autism to talk about fewer topics, and more repeatedly, during their conversations. We thus hypothesize a higher semantic overlap ratio between dialogue turns in children with ASD compared to those with typical development (TD). Participants of this study include children ages 4-8, 44 with TD and 25 with ASD without language impairment. We apply several semantic similarity metrics to the children's dialogue turns in semi-structured conversations with examiners. We find that children with ASD have significantly more semantically overlapping turns than children with TD, across different turn intervals. These results support our hypothesis, and could provide a convenient and robust ASD-specific behavioral marker.</p>","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":"2015 ","pages":"117-123"},"PeriodicalIF":0.0,"publicationDate":"2015-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5499994/pdf/nihms707956.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35155830","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
Automated morphological analysis of clinical language samples. 临床语言样本的自动形态学分析。
Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2015-06-05
Kyle Gorman, Steven Bedrick, Géza Kiss, Eric Morley, Rosemary Ingham, Metrah Mohammad, Katina Papadakis, Jan P H van Santen
{"title":"Automated morphological analysis of clinical language samples.","authors":"Kyle Gorman,&nbsp;Steven Bedrick,&nbsp;Géza Kiss,&nbsp;Eric Morley,&nbsp;Rosemary Ingham,&nbsp;Metrah Mohammad,&nbsp;Katina Papadakis,&nbsp;Jan P H van Santen","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Quantitative analysis of clinical language samples is a powerful tool for assessing and screening developmental language impairments, but requires extensive manual transcription, annotation, and calculation, resulting in error-prone results and clinical underutilization. We describe a system that performs automated morphological analysis needed to calculate statistics such as the mean length of utterance in morphemes (MLUM), so that these statistics can be computed directly from orthographic transcripts. Estimates of MLUM computed by this system are closely comparable to those produced by manual annotation. Our system can be used in conjunction with other automated annotation techniques, such as maze detection. This work represents an important first step towards increased automation of language sample analysis, and towards attendant benefits of automation, including clinical greater utilization and reduced variability in care delivery.</p>","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":"2015 ","pages":"108-116"},"PeriodicalIF":0.0,"publicationDate":"2015-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5499995/pdf/nihms707958.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35154924","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
Automated morphological analysis of clinical language samples 临床语言样本的自动形态学分析
Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2015-06-05 DOI: 10.3115/v1/W15-1213
Kyle Gorman, Steven Bedrick, G. Kiss, E. Morley, Rosemary Ingham, Metrah Mohammed, Katina Papadakis, J. V. Santen
{"title":"Automated morphological analysis of clinical language samples","authors":"Kyle Gorman, Steven Bedrick, G. Kiss, E. Morley, Rosemary Ingham, Metrah Mohammed, Katina Papadakis, J. V. Santen","doi":"10.3115/v1/W15-1213","DOIUrl":"https://doi.org/10.3115/v1/W15-1213","url":null,"abstract":"Quantitative analysis of clinical language samples is a powerful tool for assessing and screening developmental language impairments, but requires extensive manual transcription, annotation, and calculation, resulting in error-prone results and clinical underutilization. We describe a system that performs automated morphological analysis needed to calculate statistics such as the mean length of utterance in morphemes (MLUM), so that these statistics can be computed directly from orthographic transcripts. Estimates of MLUM computed by this system are closely comparable to those produced by manual annotation. Our system can be used in conjunction with other automated annotation techniques, such as maze detection. This work represents an important first step towards increased automation of language sample analysis, and towards attendant benefits of automation, including clinical greater utilization and reduced variability in care delivery.","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":"81 1","pages":"108-116"},"PeriodicalIF":0.0,"publicationDate":"2015-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85409295","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: 7
Similarity Measures for Quantifying Restrictive and Repetitive Behavior in Conversations of Autistic Children 孤独症儿童对话中限制性和重复性行为量化的相似性测量
Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2015-06-01 DOI: 10.3115/v1/W15-1214
Masoud Rouhizadeh, R. Sproat, J. Santen
{"title":"Similarity Measures for Quantifying Restrictive and Repetitive Behavior in Conversations of Autistic Children","authors":"Masoud Rouhizadeh, R. Sproat, J. Santen","doi":"10.3115/v1/W15-1214","DOIUrl":"https://doi.org/10.3115/v1/W15-1214","url":null,"abstract":"Restrictive and repetitive behavior (RRB) is a core symptom of autism spectrum disorder (ASD) and are manifest in language. Based on this, we expect children with autism to talk about fewer topics, and more repeatedly, during their conversations. We thus hypothesize a higher semantic overlap ratio between dialogue turns in children with ASD compared to those with typical development (TD). Participants of this study include children ages 4-8, 44 with TD and 25 with ASD without language impairment. We apply several semantic similarity metrics to the children's dialogue turns in semi-structured conversations with examiners. We find that children with ASD have significantly more semantically overlapping turns than children with TD, across different turn intervals. These results support our hypothesis, and could provide a convenient and robust ASD-specific behavioral marker.","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":"53 1","pages":"117-123"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73291386","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
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