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Word centrality constrained representation for keyphrase extraction. 关键词提取的词中心性约束表示。
Zelalem Gero, Joyce C Ho
{"title":"Word centrality constrained representation for keyphrase extraction.","authors":"Zelalem Gero,&nbsp;Joyce C Ho","doi":"10.18653/v1/2021.bionlp-1.17","DOIUrl":"https://doi.org/10.18653/v1/2021.bionlp-1.17","url":null,"abstract":"<p><p>To keep pace with the increased generation and digitization of documents, automated methods that can improve search, discovery and mining of the vast body of literature are essential. Keyphrases provide a concise representation by identifying salient concepts in a document. Various supervised approaches model keyphrase extraction using local context to predict the label for each token and perform much better than the unsupervised counterparts. Unfortunately, this method fails for short documents where the context is unclear. Moreover, keyphrases, which are usually the gist of a document, need to be the central theme. We propose a new extraction model that introduces a centrality constraint to enrich the word representation of a Bidirectional long short-term memory. Performance evaluation on two publicly available datasets demonstrate our model outperforms existing state-of-the art approaches. Our model is publicly available at https://github.com/ZHgero/keyphrases_centrality.git.</p>","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":" ","pages":"155-161"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9208728/pdf/nihms-1815573.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40396966","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: 5
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
Denis Newman-Griffis, J. Lehman, C. Ros'e, H. Hochheiser
{"title":"Translational NLP: A New Paradigm and General Principles for Natural Language Processing Research","authors":"Denis Newman-Griffis, J. Lehman, C. Ros'e, H. Hochheiser","doi":"10.18653/V1/2021.NAACL-MAIN.325","DOIUrl":"https://doi.org/10.18653/V1/2021.NAACL-MAIN.325","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":"21 1","pages":"4125-4138"},"PeriodicalIF":0.0,"publicationDate":"2021-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86528175","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: 11
Paragraph-level Simplification of Medical Texts 医学文本的分段简化
Ashwin Devaraj, I. Marshall, Byron C. Wallace, J. Li
{"title":"Paragraph-level Simplification of Medical Texts","authors":"Ashwin Devaraj, I. Marshall, Byron C. Wallace, J. Li","doi":"10.18653/V1/2021.NAACL-MAIN.395","DOIUrl":"https://doi.org/10.18653/V1/2021.NAACL-MAIN.395","url":null,"abstract":"We consider the problem of learning to simplify medical texts. This is important because most reliable, up-to-date information in biomedicine is dense with jargon and thus practically inaccessible to the lay audience. Furthermore, manual simplification does not scale to the rapidly growing body of biomedical literature, motivating the need for automated approaches. Unfortunately, there are no large-scale resources available for this task. In this work we introduce a new corpus of parallel texts in English comprising technical and lay summaries of all published evidence pertaining to different clinical topics. We then propose a new metric based on likelihood scores from a masked language model pretrained on scientific texts. We show that this automated measure better differentiates between technical and lay summaries than existing heuristics. We introduce and evaluate baseline encoder-decoder Transformer models for simplification and propose a novel augmentation to these in which we explicitly penalize the decoder for producing “jargon” terms; we find that this yields improvements over baselines in terms of readability.","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":"7 1","pages":"4972-4984"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78695199","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: 47
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
Denis Newman-Griffis, Venkatesh Sivaraman, Adam Perer, E. Fosler-Lussier, H. Hochheiser
{"title":"TextEssence: A Tool for Interactive Analysis of Semantic Shifts Between Corpora","authors":"Denis Newman-Griffis, Venkatesh Sivaraman, Adam Perer, E. Fosler-Lussier, H. Hochheiser","doi":"10.18653/v1/2021.naacl-demos.13","DOIUrl":"https://doi.org/10.18653/v1/2021.naacl-demos.13","url":null,"abstract":"Embeddings of words and concepts capture syntactic and semantic regularities of language; however, they have seen limited use as tools to study characteristics of different corpora and how they relate to one another. We introduce TextEssence, an interactive system designed to enable comparative analysis of corpora using embeddings. TextEssence includes visual, neighbor-based, and similarity-based modes of embedding analysis in a lightweight, web-based interface. We further propose a new measure of embedding confidence based on nearest neighborhood overlap, to assist in identifying high-quality embeddings for corpus analysis. A case study on COVID-19 scientific literature illustrates the utility of the system. TextEssence can be found at https://textessence.github.io.","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":"48 1","pages":"106-115"},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87979469","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
Trialstreamer: Mapping and Browsing Medical Evidence in Real-Time. Trialstreamer:实时绘制和浏览医学证据。
Benjamin E Nye, Ani Nenkova, Iain J Marshall, Byron C Wallace
{"title":"<i>Trialstreamer</i>: Mapping and Browsing Medical Evidence in Real-Time.","authors":"Benjamin E Nye,&nbsp;Ani Nenkova,&nbsp;Iain J Marshall,&nbsp;Byron C Wallace","doi":"10.18653/v1/2020.acl-demos.9","DOIUrl":"https://doi.org/10.18653/v1/2020.acl-demos.9","url":null,"abstract":"<p><p>We introduce <i>Trialstreamer</i>, a living database of clinical trial reports. Here we mainly describe the <i>evidence extraction</i> component; this extracts from biomedical abstracts key pieces of information that clinicians need when appraising the literature, and also the relations between these. Specifically, the system extracts descriptions of trial participants, the treatments compared in each arm (the <i>interventions</i>), and which outcomes were measured. The system then attempts to infer which interventions were reported to work best by determining their relationship with identified trial outcome measures. In addition to summarizing individual trials, these extracted data elements allow automatic synthesis of results across many trials on the same topic. We apply the system at scale to all reports of randomized controlled trials indexed in MEDLINE, powering the automatic generation of <i>evidence maps</i>, which provide a global view of the efficacy of different interventions combining data from all relevant clinical trials on a topic. We make all code and models freely available alongside a demonstration of the web interface.</p>","PeriodicalId":74542,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting","volume":"2020 ","pages":"63-69"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8204713/pdf/nihms-1593346.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39239461","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
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。
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。
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
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。
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。
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
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