Package: cognitivefactory-interactive-clustering-gui
Interactive Clustering GUIA web application designed for NLP data annotation using Interactive Clustering methodology.Quick descriptionInteractive clusteringis a method intended to assist in the design of a training data set.This iterative process begins with an unlabeled dataset, and it uses a sequence of two substeps :the user defines constraints on data sampled by the computer ;the computer performs data partitioning using a constrained clustering algorithm.Thus, at each step of the process :the user corrects the clustering of the previous steps using constraints, andthe computer offers a corrected and more relevant data partitioning for the next step.This web application implements this annotation methodology with several features:data preprocessing and vectorizationin order to reduce noise in data;constrainted clusteringin order to automatically partition the data;constraints samplingin order to select the most relevant data to annotate;binary constraints annotationin order to correct clustering relevance;annotation review and conflicts analysisin order to improve constraints consistency.For more details, read theDocumentationand the articles in theReferencessection.DocumentationMain documentationRequirementsInteractive Clustering GUI requires Python 3.8 or above.To install withpip:# install packagepython3-mpipinstallcognitivefactory-interactive-clustering-gui# install spacy language model dependencies (the one you want, with version "3.1.x")python3-mspacydownloadfr_core_news_md-3.1.0--directTo install withpipx:# install pipxpython3-mpipinstall--userpipx# install packagepipxinstall--pythonpython3cognitivefactory-interactive-clustering-gui# install spacy language model dependencies (the one you want, with version "3.1.x")python3-mspacydownloadfr_core_news_md-3.1.0--directRunTo display the help message:cognitivefactory-interactive-clustering-gui--helpTo launch the web application:cognitivefactory-interactive-clustering-gui# launch on 127.0.0.1:8080Then, go to one of the following pages in your browser:Welcome page (web application home):http://localhost:8080Swagger (interactive documentation):http://localhost:8080/docsDevelopmentTo work on this project or contribute to it, please read:theCopier PDMtemplate documentation ;theContributingpage for environment setup and development help ;theCode of Conductpage for contribution rules.ReferencesInteractive Clustering:First presentation:Schild, E., Durantin, G., Lamirel, J.C., & Miconi, F. (2021). Conception itérative et semi-supervisée d'assistants conversationnels par regroupement interactif des questions. In EGC 2021 - 21èmes Journées Francophones Extraction et Gestion des Connaissances. Edition RNTI. ⟨hal-03133007⟩.Theoretical study:Schild, E., Durantin, G., Lamirel, J., & Miconi, F. (2022). Iterative and Semi-Supervised Design of Chatbots Using Interactive Clustering. International Journal of Data Warehousing and Mining (IJDWM), 18(2), 1-19. http://doi.org/10.4018/IJDWM.298007. ⟨hal-03648041⟩.Methodological discussion:Schild, E., Durantin, G., & Lamirel, J.C. (2021). Concevoir un assistant conversationnel de manière itérative et semi-supervisée avec le clustering interactif. In Atelier - Fouille de Textes - Text Mine 2021 - En conjonction avec EGC 2021. ⟨hal-03133060⟩.Implementation:Schild, E. (2021). cognitivefactory/interactive-clustering. Zenodo. https://doi.org/10.5281/zenodo.4775251.Web application:FastAPI:https://fastapi.tiangolo.com/How to citeSchild, E. (2021). cognitivefactory/interactive-clustering-gui. Zenodo. https://doi.org/10.5281/zenodo.4775270.