652 Hits in 4.1 sec

Speculation and Negation Scope Detection via Convolutional Neural Networks

Zhong Qian, Peifeng Li, Qiaoming Zhu, Guodong Zhou, Zhunchen Luo, Wei Luo
2016 Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing  
In this paper, we propose a Convolutional Neural Network (CNN)-based model with probabilistic weighted average pooling to address speculation and negation scope detection.  ...  In particular, our CNN-based model extracts those meaningful features from various syntactic paths between the cues and the candidate tokens in both constituency and dependency parse trees.  ...  In addition, thanks to the three anonymous reviewers for their valuable comments.  ... 
doi:10.18653/v1/d16-1078 dblp:conf/emnlp/QianLZZLL16 fatcat:4fgjcmjiwjfddfzwlcz4ddyidy

Neural Token Representations and Negation and Speculation Scope Detection in Biomedical and General Domain Text

Elena Sergeeva, Henghui Zhu, Amir Tahmasebi, Peter Szolovits
2019 Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)  
In this paper, we investigate the application and impact of state-of-the-art neural token representations for automatic cueconditional speculation and negation scope detection coupled with the independently  ...  Furthermore, we provide a thorough analysis of neural representations and additional features interactions, cue-representation for conditioning, discussing model behavior on different datasets and, finally  ...  ., 2010) as well as hybrid CRF-SVM ensemble models (Zhu et al., 2010) (Morante and Daelemans, 2009) Recently, Neural Network-based approaches have been proposed for such tasks, including Convolutional  ... 
doi:10.18653/v1/d19-6221 dblp:conf/acl-louhi/SergeevaZTS19 fatcat:xz7kdlrvungizivzzdp6bf67ae

Negation and Speculation in NLP: A Survey, Corpora, Methods, and Applications

Ahmed Mahany, Heba Khaled, Nouh Sabri Elmitwally, Naif Aljohani, Said Ghoniemy
2022 Applied Sciences  
Furthermore, we discuss the ongoing research into recent rule-based, supervised, and transfer learning techniques for the detection of negating and speculative content.  ...  Adding more syntactic features may alleviate the limitations of the existing techniques, such as cue ambiguity and detecting the discontinuous scopes.  ...  In another study, syntactic path-based hybrid architecture combines the BiLSTM and CNN networks [39] .  ... 
doi:10.3390/app12105209 fatcat:jzm5hjhcqbbr5ck6cosat7n5zq

Speculation and Negation Detection in French Biomedical Corpora

Clément Dalloux, University Rennes, Inria, CNRS, IRISA,Rennes, France, Vincent Claveau, Natalia Grabar, University Rennes, Inria, CNRS, IRISA,Rennes, France, UMR 8163 STL CNRS, Université de Lille, France
2019 Proceedings - Natural Language Processing in a Deep Learning World  
In this work, we propose to address the detection of negation and speculation, and of their scope, in French biomedical documents.  ...  We reach up to 97.21 % and 91.30 % F-measure for the detection of negation and speculation cues, respectively, using CRFs.  ...  ACKNOWLEDGMENTS This work was partly funded by the French government support granted to the CominLabs LabEx managed by the ANR in Investing for the Future program under reference ANR-10-LABX-07-01.  ... 
doi:10.26615/978-954-452-056-4_026 dblp:conf/ranlp/DallouxCG19 fatcat:z7lf3jwczjbzjhokmeulpp4mtq

NeuralLog: Natural Language Inference with Joint Neural and Logical Reasoning [article]

Zeming Chen, Qiyue Gao, Lawrence S. Moss
2021 arXiv   pre-print
To merge symbolic and deep learning methods, we propose an inference framework called NeuralLog, which utilizes both a monotonicity-based logical inference engine and a neural network language model for  ...  Our framework models the NLI task as a classic search problem and uses the beam search algorithm to search for optimal inference paths.  ...  Acknowledgements We thank the anonymous reviewers for their insightful comments. We also thank Dr. Michael Wollowski from Rose-hulman Institute of Technology for his helpful feedback on this paper.  ... 
arXiv:2105.14167v3 fatcat:clpvn6m5hnd6zemyh4k7tsxtwa

Scope resolution of predicted negation cues: A two-step neural network-based approach [article]

Daan de Jong
2021 arXiv   pre-print
Neural network-based methods are the state of the art in negation scope resolution. However, they often use the unrealistic assumption that cue information is completely accurate.  ...  The current study adopted a two-step negation resolving apporach to assess whether a Bidirectional Long Short-Term Memory-based method can be used for cue detection as well, and how inaccurate cue predictions  ...  Conclusion and future research The current study adopted a neural network-based approach to both sub tasks of negation resolving: cue detection and scope resolution.  ... 
arXiv:2109.07264v1 fatcat:poean2ibdbdehcc7xrlpmn7ijm

Negation-Instance Based Evaluation of End-to-End Negation Resolution [article]

Elizaveta Sineva, Stefan Grünewald, Annemarie Friedrich, Jonas Kuhn
2021 arXiv   pre-print
Examining the problem both from a linguistic perspective and from a downstream viewpoint, we here argue for a negation-instance based approach to evaluating negation resolution.  ...  In this paper, we revisit the task of negation resolution, which includes the subtasks of cue detection (e.g. "not", "never") and scope resolution.  ...  Association for Computa- tional Linguistics. Lydia Lazib, Bing Qin, Yanyan Zhao, Weinan Zhang, and Ting Liu. 2020. A syntactic path-based hybrid neural network for negation scope detection.  ... 
arXiv:2109.10013v1 fatcat:gp6t5ibk2bb2nogpvstslo2ani

Classifying the reported ability in clinical mobility descriptions

Denis Newman-Griffis, Ayah Zirikly, Guy Divita, Bart Desmet
2019 Proceedings of the 18th BioNLP Workshop and Shared Task  
Finally, we highlight several challenges in classifying performance assertions, including capturing information about sources of assistance, incorporating syntactic structure and negation scope, and handling  ...  Our findings establish a strong baseline for this novel task, and identify intriguing areas for further research.  ...  Acknowledgments The authors would like to thank Pei-Shu Ho, Jonathan Camacho Maldonado, and Maryanne Sacco for discussions about error analysis, and our anonymous reviewers for their helpful comments.  ... 
doi:10.18653/v1/w19-5001 dblp:conf/bionlp/Newman-GriffisZ19 fatcat:e7h4ictcmzb4tg2qacvpy7kjty

Integrating Speculation Detection and Deep Learning to Extract Lung Cancer Diagnosis from Clinical Notes

Oswaldo Solarte Pabón, Maria Torrente, Mariano Provencio, Alejandro Rodríguez-Gonzalez, Ernestina Menasalvas
2021 Applied Sciences  
To address this challenge, a hybrid approach that combines deep learning-based and rule-based methods is proposed.  ...  Our findings suggest that speculation detection is together with negation detection a key component to properly extract cancer diagnosis from clinical notes.  ...  Additionally, a fifth rule that uses syntactic parse trees for scope recognition was added.  ... 
doi:10.3390/app11020865 fatcat:odpnldls7jhetgh23zvss5lm6y

High-Precision Biomedical Relation Extraction for Reducing Human Curation Efforts in Industrial Applications

Alan Ramponi, Stefano Giampiccolo, Danilo Tomasoni, Corrado Priami, Rosario Lombardo
2020 IEEE Access  
Experiments on gold-standard corpora show that the system achieves the highest precision compared with previous rulebased, kernel-based, and neural approaches, while maintaining a F1 score comparable or  ...  However, in industrial applications relations typically serve as input to a pipeline of biologically driven analyses; as a result, highly precise extractions are central for cutting down the manual curation  ...  ACKNOWLEDGMENT We thank Samanta Michelini for the feedback on the work.  ... 
doi:10.1109/access.2020.3014862 fatcat:xhuubcxshjgvxmkjftwheivsz4

Enhanced Twitter Sentiment Analysis Using Hybrid Approach and by Accounting Local Contextual Semantic

Itisha Gupta, Nisheeth Joshi
2019 Journal of Intelligent Systems  
Additionally, we augment negation accounting procedure with a few heuristics for handling the cases in which negation presence does not necessarily mean negation.  ...  This paper addresses the problem of Twitter sentiment analysis through a hybrid approach in which SentiWordNet (SWN)-based feature vector acts as input to the classification model Support Vector Machine  ...  LSTM is a type of recurrent neural network used in deep learning field for the sequence prediction problems.  ... 
doi:10.1515/jisys-2019-0106 fatcat:5mtmmiiaavgdzp44qrdu4vjsem

Cross Disciplinary Consultancy to Bridge Public Health Technical Needs and Analytic Developers: Negation Detection Use Case

Mike Conway, Danielle Mowery, Amy Ising, Sumithra Velupillai, Son Doan, Julia Gunn, Michael Donovan, Caleb Wiedeman, Lance Ballester, Karl Soetebier, Catherine Tong, Burkom Howard
2018 Online Journal of Public Health Informatics  
of text processing algorithms to identify negated terms (i.e. negation detection) in free-text chief complaints and triage reports.  ...  The topic of this final consultancy, conducted at the University of Utah in January 2017, is focused on defining a roadmap for the development of algorithms, tools, and datasets for improving the capabilities  ...  With the emergence of deep learning (i.e. neural network-based machine learning algorithms with multiple layers), there are now several studies that have focused on using this approach for negation detection  ... 
doi:10.5210/ojphi.v10i2.8944 pmid:30349627 pmcid:PMC6194092 fatcat:lk7y42bjtzdzlp3carnqwweqoy

Semantic Compositionality through Recursive Matrix-Vector Spaces

Richard Socher, Brody Huval, Christopher D. Manning, Andrew Y. Ng
2012 Conference on Empirical Methods in Natural Language Processing  
We introduce a recursive neural network (RNN) model that learns compositional vector representations for phrases and sentences of arbitrary syntactic type and length.  ...  path between them. ... very good movie ... ( a , A ) ( b , B ) ( c , C )  ...  Acknowledgments We thank for great discussions about the paper: John Platt, Chris Potts, Josh Tenenbaum, Mihai Surdeanu, Quoc Le and Kevin Miller.  ... 
dblp:conf/emnlp/SocherHMN12 fatcat:ecjt2eamhfb3fbnjttxumge6me

Learning for Biomedical Information Extraction: Methodological Review of Recent Advances [article]

Feifan Liu, Jinying Chen, Abhyuday Jagannatha, Hong Yu
2016 arXiv   pre-print
Unlike existing reviews covering a holistic view on BioIE, this review focuses on mainly recent advances in learning based approaches, by systematically summarizing them into different aspects of methodological  ...  , e.g., deep neural networks (DNNs) [139] , convolutional neural networks (CNNs) [140] and recurrent neural networks(RNNs) [141] ; and (3) hybrid, e.g., DBN-DNN [142] models that combine unsupervised  ...  features, including lexical, syntactic, semantic and negation features derived from sentences and their corresponding parse trees.  ... 
arXiv:1606.07993v1 fatcat:7d5om7zxxzhoviiriasrfwg3xi

Interpreting Deep Learning Models in Natural Language Processing: A Review [article]

Xiaofei Sun, Diyi Yang, Xiaoya Li, Tianwei Zhang, Yuxian Meng, Han Qiu, Guoyin Wang, Eduard Hovy, Jiwei Li
2021 arXiv   pre-print
We first stretch out a high-level taxonomy for interpretation methods in NLP, i.e., training-based approaches, test-based approaches, and hybrid approaches.  ...  However, a long-standing criticism against neural network models is the lack of interpretability, which not only reduces the reliability of neural NLP systems but also limits the scope of their applications  ...  After fine-tuning on a negation scope task, the average sensitivity of attention heads toward negation scope detection improves for all model variants.  ... 
arXiv:2110.10470v2 fatcat:efbcafv5ajdlvn7347cv2d3m2a
« Previous Showing results 1 — 15 out of 652 results