{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/infodens-an-open-source-framework-for","title":"INFODENS: An Open-source Framework for Learning Text Representations","arxiv_id":"1810.07091","date":"2018-10-16","proceeding":null,"authors":["Ahmad Taie","Raphael Rubino","Josef van Genabith"],"abstract":"The advent of representation learning methods enabled large performance gains\non various language tasks, alleviating the need for manual feature engineering.\nWhile engineered representations are usually based on some linguistic\nunderstanding and are therefore more interpretable, learned representations are\nharder to interpret. Empirically studying the complementarity of both\napproaches can provide more linguistic insights that would help reach a better\ncompromise between interpretability and performance. We present INFODENS, a\nframework for studying learned and engineered representations of text in the\ncontext of text classification tasks. It is designed to simplify the tasks of\nfeature engineering as well as provide the groundwork for extracting learned\nfeatures and combining both approaches. INFODENS is flexible, extensible, with\na short learning curve, and is easy to integrate with many of the available and\nwidely used natural language processing tools.","url_abs":"http://arxiv.org/abs/1810.07091v1","url_pdf":"http://arxiv.org/pdf/1810.07091v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"infodens-an-open-source-framework-for","repo_url":"https://github.com/ahmad-taie/infodens","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}