Procode: the Swiss Multilingual Solution for Automatic Coding and Recoding of Occupations and Economic Activities

• URL: http://arxiv.org/abs/2012.07521v1
• Date: Mon, 30 Nov 2020 07:46:21 GMT
• Title: Procode: the Swiss Multilingual Solution for Automatic Coding and Recoding of Occupations and Economic Activities
• Abstract summary: Epidemiological studies require data that is in alignment with the classifications established for occupations or economic activities. The goal was to develop and test a web-tool, named Procode, for coding of free-texts against classifications and recoding between different classifications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Objective. Epidemiological studies require data that are in alignment with the classifications established for occupations or economic activities. The classifications usually include hundreds of codes and titles. Manual coding of raw data may result in misclassification and be time consuming. The goal was to develop and test a web-tool, named Procode, for coding of free-texts against classifications and recoding between different classifications. Methods. Three text classifiers, i.e. Complement Naive Bayes (CNB), Support Vector Machine (SVM) and Random Forest Classifier (RFC), were investigated using a k-fold cross-validation. 30 000 free-texts with manually assigned classification codes of French classification of occupations (PCS) and French classification of activities (NAF) were available. For recoding, Procode integrated a workflow that converts codes of one classification to another according to existing crosswalks. Since this is a straightforward operation, only the recoding time was measured. Results. Among the three investigated text classifiers, CNB resulted in the best performance, where the classifier predicted accurately 57-81% and 63-83% classification codes for PCS and NAF, respectively. SVM lead to somewhat lower results (by 1-2%), while RFC coded accurately up to 30% of the data. The coding operation required one minute per 10 000 records, while the recoding was faster, i.e. 5-10 seconds. Conclusion. The algorithm integrated in Procode showed satisfactory performance, since the tool had to assign the right code by choosing between 500-700 different choices. Based on the results, the authors decided to implement CNB in Procode. In future, if another classifier shows a superior performance, an update will include the required modifications.

Related papers

• SHELF: A Synthetic Harness for Multi-Task Bibliographic Benchmarking [0.0]
  SHELF is a Python system that turns labelled budget, writing specifications, and a generation of data into benchmark data and evaluation tasks.<n>This first release contains 62,899 model-written documents based on Library of Congress vocabularies.<n>We compare TFBench, TF-IDF, BM25, popular encoders, and, on subject classification only, zero-shot decoders.
  arXiv  Detail & Related papers  (2026-09-02T18:17:41Z)
• ReLeVAnT: Relevance Lexical Vectors for Accurate Legal Text Classification [5.530730417910086]
  ReLeVAnT is a framework for legal document binary classification.<n>It uses n-gram processing, contrastive score matching, and a shallow neural network as the primary drivers for discriminative classification.<n>It has 99.3% accuracy and 98.7% F1 score on the LexGLUE dataset.
  arXiv  Detail & Related papers  (2026-04-24T07:13:24Z)
• Retrieve, Then Classify: Corpus-Grounded Automation of Clinical Value Set Authoring [1.3108798582758454]
  Clinical value set authoring is a recurring bottleneck in clinical quality measurement and phenotyping.<n>We propose Retrieval-Augmented Set Completion (RASC): retrieve the $K$ most similar existing value sets from a curated corpus to form a candidate pool.<n>We demonstrate the utility of RASC on 11,803 publicly available VSAC value sets, constructing the first large-scale benchmark for this task.
  arXiv  Detail & Related papers  (2026-04-16T04:57:21Z)
• AlgoVeri: An Aligned Benchmark for Verified Code Generation on Classical Algorithms [54.99368693313797]
  Existing benchmarks test only individual languages/tools, so the performance numbers are not directly comparable.<n>We address this gap with AlgoVeri, a benchmark that evaluates vericoding of $77$ classical algorithms in Dafny, Verus, and Lean.
  arXiv  Detail & Related papers  (2026-02-10T06:58:26Z)
• Blueprinting the Future: Automatic Item Categorization using Hierarchical Zero-Shot and Few-Shot Classifiers [6.907552533477328]
  This study unveils a novel approach employing the zero-shot and few-shot Generative Pretrained Transformer (GPT) for hierarchical item categorization. The hierarchical nature of examination blueprints is navigated seamlessly, allowing for a tiered classification of items across multiple levels. An initial simulation with artificial data demonstrates the efficacy of this method, achieving an average accuracy of 92.91% measured by the F1 score.
  arXiv  Detail & Related papers  (2023-12-06T15:51:49Z)
• Leveraging Generative AI: Improving Software Metadata Classification with Generated Code-Comment Pairs [0.0]
  In software development, code comments play a crucial role in enhancing code comprehension and collaboration. This research paper addresses the challenge of objectively classifying code comments as "Useful" or "Not Useful" We propose a novel solution that harnesses contextualized embeddings, particularly BERT, to automate this classification process.
  arXiv  Detail & Related papers  (2023-10-14T12:09:43Z)
• Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment [53.2701026843921]
  Large-scale pre-trained Vision Language Models (VLMs) have proven effective for zero-shot classification. In this paper, we aim at a more challenging setting, Realistic Zero-Shot Classification, which assumes no annotation but instead a broad vocabulary. We propose the Self Structural Semantic Alignment (S3A) framework, which extracts structural semantic information from unlabeled data while simultaneously self-learning.
  arXiv  Detail & Related papers  (2023-08-24T17:56:46Z)
• ProTeCt: Prompt Tuning for Taxonomic Open Set Classification [59.59442518849203]
  Few-shot adaptation methods do not fare well in the taxonomic open set (TOS) setting. We propose a prompt tuning technique that calibrates the hierarchical consistency of model predictions. A new Prompt Tuning for Hierarchical Consistency (ProTeCt) technique is then proposed to calibrate classification across label set granularities.
  arXiv  Detail & Related papers  (2023-06-04T02:55:25Z)
• A Systematic Literature Review of Automated ICD Coding and Classification Systems using Discharge Summaries [5.156484100374058]
  Codification of free-text clinical narratives has long been recognised to be beneficial for secondary uses such as funding, insurance claim processing and research. The current scenario of assigning codes is a manual process which is very expensive, time-consuming and error prone. This systematic literature review provides a comprehensive overview of automated clinical coding systems.
  arXiv  Detail & Related papers  (2021-07-12T03:55:17Z)
• No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data [78.69828864672978]
  A central challenge in training classification models in the real-world federated system is learning with non-IID data. We propose a novel and simple algorithm called Virtual Representations (CCVR), which adjusts the classifier using virtual representations sampled from an approximated ssian mixture model. Experimental results demonstrate that CCVR state-of-the-art performance on popular federated learning benchmarks including CIFAR-10, CIFAR-100, and CINIC-10.
  arXiv  Detail & Related papers  (2021-06-09T12:02:29Z)
• CoSQA: 20,000+ Web Queries for Code Search and Question Answering [63.92224685262063]
  CoSQA dataset includes 20,604 labels for pairs of natural language queries and codes. We introduce a contrastive learning method dubbed CoCLR to enhance query-code matching. We show that evaluated on CodeXGLUE with the same CodeBERT model, training on CoSQA improves the accuracy of code question answering by 5.1%.
  arXiv  Detail & Related papers  (2021-05-27T15:37:21Z)
• Improving Calibration for Long-Tailed Recognition [68.32848696795519]
  We propose two methods to improve calibration and performance in such scenarios. For dataset bias due to different samplers, we propose shifted batch normalization. Our proposed methods set new records on multiple popular long-tailed recognition benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-01T13:55:21Z)
• TransICD: Transformer Based Code-wise Attention Model for Explainable ICD Coding [5.273190477622007]
  International Classification of Disease (ICD) coding procedure has been shown to be effective and crucial to the billing system in medical sector. Currently, ICD codes are assigned to a clinical note manually which is likely to cause many errors. In this project, we apply a transformer-based architecture to capture the interdependence among the tokens of a document and then use a code-wise attention mechanism to learn code-specific representations of the entire document.
  arXiv  Detail & Related papers  (2021-03-28T05:34:32Z)
• InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees [17.461451218469062]
  This paper proposes InferCode to overcome the limitation by adapting the self-language learning mechanism to build source code model. Subtrees in ASTs are treated with InferCode as the labels for training code representations without any human labeling effort or the overhead of expensive graph construction. Compared to previous code learning techniques applied to the same downstream tasks, such as Code2Vec, Code2Seq, ASTNN, higher performance results are achieved using our pre-trained InferCode model.
  arXiv  Detail & Related papers  (2020-12-13T10:33:41Z)
• Searching towards Class-Aware Generators for Conditional Generative Adversarial Networks [132.29772160843825]
  Conditional Generative Adversarial Networks (cGAN) were designed to generate images based on the provided conditions. Existing methods have used the same generating architecture for all classes. This paper presents a novel idea that adopts NAS to find a distinct architecture for each class.
  arXiv  Detail & Related papers  (2020-06-25T07:05:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.