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A machine-learning approach to negation and speculation detection in clinical texts

Noa P. Cruz Díaz, Manuel J. Maña López, Jacinto Mata Vázquez, Victoria Pachón Álvarez
2012 Journal of the American Society for Information Science and Technology  
Our research is focused on developing a system based on machine-learning techniques that identifies negation and speculation signals and their scope in clinical texts.  ...  In the signal detection task, the F-score value was 97.3% in negation and 94.9% in speculation.  ...  We would like to thank four anonymous reviewers for their valuable suggestions.  ... 
doi:10.1002/asi.22679 fatcat:ehdihsybzjcgzpokloeys5v6bi

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.  ...  In this article, we review the corpora annotated with negation and speculation in various natural languages and domains.  ...  Cruz et al. proposed a machine learning approach to detect these problems in the review domain in Spanish [32] .  ... 
doi:10.3390/app12105209 fatcat:jzm5hjhcqbbr5ck6cosat7n5zq

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  
The approach integrates three steps: (i) lung cancer named entity recognition, (ii) negation and speculation detection, and (iii) relating the cancer diagnosis to a valid date.  ...  Our findings suggest that speculation detection is together with negation detection a key component to properly extract cancer diagnosis from clinical notes.  ...  This section shows a rule-based approach to detect negation and speculation in clinical texts written in Spanish.  ... 
doi:10.3390/app11020865 fatcat:odpnldls7jhetgh23zvss5lm6y

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

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  ...  , address the annotation-induced biases in the learned representations.  ...  It is, however, important to note that due to the similarity in the formulation of the task, the majority of the negation-specific machine learning approaches can be directly applied to the speculation  ... 
doi:10.18653/v1/d19-6221 dblp:conf/acl-louhi/SergeevaZTS19 fatcat:xz7kdlrvungizivzzdp6bf67ae

Basic Building Blocks for Clinical Text Processing [chapter]

Hercules Dalianis
2018 Clinical Text Mining  
However, clinical texts contain more noise in the form of incomplete sentences, misspelled words and non-standard abbreviations that can make the natural language processing cumbersome.  ...  Generally, the same building blocks used for regular texts can also be utilised for clinical text processing.  ...  Machine Learning Approaches for Negation Detection In a machine learning approach, annotated corpora of Swedish and English negation cues were used as training and evaluation data for the Stanford NER  ... 
doi:10.1007/978-3-319-78503-5_7 fatcat:pqwyc2vm2zh2vlky7bzo5da3pa

Modality and Negation: An Introduction to the Special Issue

Roser Morante, Caroline Sporleder
2012 Computational Linguistics  
Researchers have started to work on modeling factuality, belief and certainty, detecting speculative sentences and hedging, identifying contradictions, and determining the scope of expressions of modality  ...  In this article, we will provide an overview of how modality and negation have been modeled in computational linguistics.  ...  Systems differ in the number of class labels used as a target and in the machine learning approaches applied.  ... 
doi:10.1162/coli_a_00095 fatcat:6p6vlzsrnfglve7fupa5ahykmu

A machine-learning approach to negation and speculation detection for sentiment analysis

Noa P. Cruz, Maite Taboada, Ruslan Mitkov
2015 Journal of the Association for Information Science and Technology  
This paper presents a machine-learning approach to automatically detect this kind of information in the review domain.  ...  In speculation, the performance obtained in the cue prediction phase is close to that obtained by a human rater carrying out the same task.  ...  Acknowledgments This work was supported in part by a grant to Maite Taboada from the Natural Sciences and Engineering Research Council of Canada (Discovery Grant 261104-2008).  ... 
doi:10.1002/asi.23533 fatcat:nmfcjwqoqvgidlbqrgidh2hnlq

Improving negation detection with negation-focused pre-training [article]

Thinh Hung Truong, Timothy Baldwin, Trevor Cohn, Karin Verspoor
2022 arXiv   pre-print
Negation is a common linguistic feature that is crucial in many language understanding tasks, yet it remains a hard problem due to diversity in its expression in different types of text.  ...  Extensive experiments on common benchmarks show that our proposed approach improves negation detection performance and generalizability over the strong baseline NegBERT (Khandewal and Sawant, 2020).  ...  This research was conducted by the Australian Research Council Training Centre in Cognitive Computing for Medical Technologies (project number ICI70200030) and funded by the Australian Government.  ... 
arXiv:2205.04012v1 fatcat:rvn5ofkkcre4hoat5tsjq2zbre

Negation and Speculation Target Identification [chapter]

Bowei Zou, Guodong Zhou, Qiaoming Zhu
2014 Communications in Computer and Information Science  
Negation and speculation are common in natural language text.  ...  Evaluation justifies the effectiveness of our proposed approach on negation and speculation target identification in biomedical texts.  ...  While earlier studies adopt rule-based approaches (e.g., Light et al., 2004) , machine learning-based approaches begin to dominate the research on negation and speculation (e.g., Morante et al., 2008  ... 
doi:10.1007/978-3-662-45924-9_4 fatcat:55gtglmh6rarnk4jqfiqfonjym

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  
Speculation and negation are important information to identify text factuality.  ...  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 addition, thanks to the three anonymous reviewers for their valuable comments.  ... 
doi:10.18653/v1/d16-1078 dblp:conf/emnlp/QianLZZLL16 fatcat:4fgjcmjiwjfddfzwlcz4ddyidy

Negation and uncertainty detection in clinical texts written in Spanish: a deep learning-based approach

Oswaldo Solarte Pabón, Orlando Montenegro, Maria Torrente, Alejandro Rodríguez González, Mariano Provencio, Ernestina Menasalvas
2022 PeerJ Computer Science  
In this paper, we propose a deep learning-based approach for both negation and uncertainty detection in clinical texts written in Spanish.  ...  The proposed approach shows the feasibility of deep learning-based methods to detect negation and uncertainty in Spanish clinical texts.  ...  Machine learning-based approaches In machine learning-based approaches, negation and uncertainty detection is formulated as a classification problem where both cues and scope detection are considered as  ... 
doi:10.7717/peerj-cs.913 pmid:35494817 pmcid:PMC9044225 fatcat:wuk6oblpdva6zfzt6a4ie6stzm

NegFinder: A Web Service for Identifying Negation Signals and Their Scopes

Kazuki Fujikawa, Kazuhiro Seki, Kuniaki Uehara
2013 IPSJ Transactions on Bioinformatics  
An obstacle in finding information in natural language text is negations, which deny or reverse the meaning of a sentence.  ...  This paper reports on our work on a hybrid approach to negation identification combining statistical and heuristic approaches and describes an implementation of the approach, named NegFinder, as a Web  ...  The latter takes advantage of machine learning techniques and classifies whether each token of input text is a negated expression.  ... 
doi:10.2197/ipsjtbio.6.29 fatcat:wmfmtcf4tnb6phgeuteb2glezi

Identifying adverse drug event information in clinical notes with distributional semantic representations of context

Aron Henriksson, Maria Kvist, Hercules Dalianis, Martin Duneld
2015 Journal of Biomedical Informatics  
In this study, we report on the creation of an annotated corpus of Swedish health records for the purpose of learning to identify information pertaining to ADEs present in clinical notes.  ...  To this end, three key tasks are tackled: recognizing relevant named entities (disorders, symptoms, drugs), labeling attributes of the recognized entities (negation, speculation, temporality), and relationships  ...  Acknowledgements This work was partly supported by the project High-Performance Data Mining for Drug Effect Detection at Stockholm  ... 
doi:10.1016/j.jbi.2015.08.013 pmid:26291578 fatcat:jyfstp5jrrewpp5r2knmzobgyq

A Review on Negation Role in Twitter Sentiment Analysis

Itisha Gupta, Nisheeth Joshi
2021 International Journal of Healthcare Information Systems and Informatics  
The authors discuss the various approaches of modelling negation in Twitter sentiment analysis. In particular, their focus is on negation scope detection and negation handling methods.  ...  In this paper, the authors present a survey on the negation role that has been done until now in sentiment analysis, specifically Twitter sentiment analysis.  ...  corpus Machine learning, lexicon and Best F1 score: 82.56 with machine learning syntax-based approach for negation approach detection Attardi et al.  ... 
doi:10.4018/ijhisi.20211001.oa14 fatcat:ano35ozjb5chrfbrhz5cyxnk2a
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