skweak: Weak Supervision Made Easy for NLP

Lison, Pierre; Barnes, Jeremy; Hubin, Aliaksandr

doi:10.18653/v1/2021.acl-demo.40

Cited by 20 publications

(4 citation statements)

References 27 publications

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“…Recently, more packages and environments (e.g. Snorkel [43], skweak [50]) have been created to apply weak supervision in general domain NLP practice. Thus, a promising future study is to adapt the current weak supervision infrastructures or the ideas behind them to the clinical NLP domain and establish best practice in the field, and a recent work is Trove [42].…”

Section: Conclusion Discussion and Future Studiesmentioning

confidence: 99%

Ontology-Based and Weakly Supervised Rare Disease Phenotyping from Clinical Notes

Dong¹,

Suárez-Paniagua²,

Zhang³

et al. 2022

Preprint

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Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes. Rare diseases are challenging to be identified due to few cases available for machine learning and the need for data annotation from domain experts. We propose a method using ontologies and weak supervision, with recent pre-trained contextual representations from Bidirectional Transformers (e.g. BERT). The ontology-based framework includes two steps: (i) Text-to-UMLS, extracting phenotypes by contextually linking mentions to concepts in Unified Medical Language System (UMLS), with a Named Entity Recognition and Linking (NER+L) tool, SemEHR, and weak supervision with customised rules and contextual mention representation; (ii) UMLS-to-ORDO, matching UMLS concepts to rare diseases in Orphanet Rare Disease Ontology (ORDO). The weakly supervised approach is proposed to learn a phenotype confirmation model to improve Text-to-UMLS linking, without annotated data from domain experts. We evaluated the approach on three clinical datasets of discharge summaries and radiology reports from two institutions in the US and the UK. Our best weakly supervised method achieved 81.4% precision and 91.4% recall on extracting rare disease UMLS phenotypes from MIMIC-III discharge summaries. The overall pipeline processing clinical notes can surface rare disease cases, mostly uncaptured in structured data (manually assigned ICD codes). Results on radiology reports from MIMIC-III and NHS Tayside were consistent with the discharge summaries. We discuss the usefulness of the weak supervision approach and propose directions for future studies.

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Section: Conclusion Discussion and Future Studiesmentioning

confidence: 99%

Ontology-Based and Weakly Supervised Rare Disease Phenotyping from Clinical Notes

Dong¹,

Suárez-Paniagua²,

Zhang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Work in progress aims to extend this to other types of entities (locations and social media links) and approach the problem in a multitask framework to leverage the connections in the output data (e.g., person names might be part of social media links too, but are unlikely to be part of a location). We also aim to improve the connections between the different components using a weak supervision framework, particularly Skweak [12].…”

Section: Phase 1: Specific Applications and Domainsmentioning

confidence: 99%

Better Bridges Between Model and RealWorld

Pelrine

2023

Proceedings of the Canadian Conference on Artificial Intelligence

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To build better machine learning solutions, we need not only better models but also better bridges through the inputs and outputs to the real world challenges they aim to solve. On the input side, these bridges are the tools one has to work with the available data. On the output side, they are the tools to evaluate and ensure models will generalize and work as intended. This project aims to improve our understanding and resources on both sides, with a particular focus on social good applications. So far, progress has been made in 6 contexts, such as countering misinformation and human trafficking. Future work aims to both build on these specific contexts, as well as leverage their interconnections to produce insights and tools with wide applicability.

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“…When creating a dataset to train our model, we need to define the assignment of labels by existing linguistic properties of each class. To assign speech act annotations to linguistic expressions containing the linguistic properties just mentioned, we use the weak supervision library skweak [22]. This library enables the generation of so-called weak labels through annotator functions.…”

Section: B Heuristic Labelling Using Weak Supervisionmentioning

confidence: 99%

Classifying Speech Acts in Political Communication: A Transformer-based Approach with Weak Supervision and Active Learning

Schmidt,

Niekler,

Kantner

et al. 2023

Annals of Computer Science and Information Systems

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We present a study on the automatic classification of speech acts in the domain of political communication, based on J. R. Searle's classification of illocutionary acts. Our research involves creating a dataset using the US State of the Union corpus and the UN General Debate corpus (UNGD) as data sources.To overcome limited labelled data, we employ a combination of weak supervision and active learning techniques for dataset creation and model training. Through various experiments, we investigate the influence of external and internal factors on speech act classification. In addition, we discuss the potential for further analysis of speech act usage, using the trained model on the UNGD corpus. The findings demonstrate the effectiveness of Transformer-based models for automatic speech act classification, highlight the benefits of weak supervision and active learning for dataset creation and model training, and underscore the potential for large-scale statistical analysis of speech act usage in the domain of political communication.

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skweak: Weak Supervision Made Easy for NLP

Cited by 20 publications

References 27 publications

Ontology-Based and Weakly Supervised Rare Disease Phenotyping from Clinical Notes

Ontology-Based and Weakly Supervised Rare Disease Phenotyping from Clinical Notes

Better Bridges Between Model and RealWorld

Classifying Speech Acts in Political Communication: A Transformer-based Approach with Weak Supervision and Active Learning

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