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A machine-learning approach to negation and speculation detection in clinical texts
2012
Journal of the American Society for Information Science and Technology
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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
2022
Applied Sciences
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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
2021
Applied Sciences
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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
2019
Proceedings - Natural Language Processing in a Deep Learning World
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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
2019
Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)
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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]
2018
Clinical Text Mining
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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
2012
Computational Linguistics
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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
2015
Journal of the Association for Information Science and Technology
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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]
2022
arXiv
pre-print
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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]
2014
Communications in Computer and Information Science
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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
2016
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
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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
2022
PeerJ Computer Science
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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
2013
IPSJ Transactions on Bioinformatics
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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
2015
Journal of Biomedical Informatics
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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
2021
International Journal of Healthcare Information Systems and Informatics
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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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