{"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/convtexttm-an-explainable-convolutional","title":"ConvTextTM: An Explainable Convolutional Tsetlin Machine Framework for Text Classification","arxiv_id":null,"date":"2022-06-01","proceeding":"LREC 2022 6","authors":["Bimal Bhattarai","Ole-Christoffer Granmo","Lei Jiao"],"abstract":"Recent advancements in natural language processing (NLP) have reshaped the industry, with powerful language models such as GPT-3 achieving superhuman performance on various tasks. However, the increasing complexity of such models turns them into “black boxes”, creating uncertainty about their internal operation and decision-making. Tsetlin Machine (TM) employs human-interpretable conjunctive clauses in propositional logic to solve complex pattern recognition problems and has demonstrated competitive performance in various NLP tasks. In this paper, we propose ConvTextTM, a novel convolutional TM architecture for text classification. While legacy TM solutions treat the whole text as a corpus-specific set-of-words (SOW), ConvTextTM breaks down the text into a sequence of text fragments. The convolution over the text fragments opens up for local position-aware analysis. Further, ConvTextTM eliminates the dependency on a corpus-specific vocabulary. Instead, it employs a generic SOW formed by the tokenization scheme of the Bidirectional Encoder Representations from Transformers (BERT). The convolution binds together the tokens, allowing ConvTextTM to address the out-of-vocabulary problem as well as spelling errors. We investigate the local explainability of our proposed method using clause-based features. Extensive experiments are conducted on seven datasets, to demonstrate that the accuracy of ConvTextTM is either superior or comparable to state-of-the-art baselines.","url_abs":"https://aclanthology.org/2022.lrec-1.401","url_pdf":"https://aclanthology.org/2022.lrec-1.401.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":[],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"document-classification","task_name":"Document Classification"},{"task_slug":"fake-news-detection","task_name":"Fake News Detection"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/document-classification-on-wos-5736","task":"Document Classification","dataset":"WOS-5736","model":"ConvTextTM","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"91.28"},"uses_additional_data":false},{"leaderboard":"/sota/fake-news-detection-on-politifact","task":"Fake News Detection","dataset":"PolitiFact","model":"Convolutional Tsetlin Machine","rank_in_archive_order":1,"of":1,"metrics":{"1:1 Accuracy":"91.21"},"uses_additional_data":false},{"leaderboard":"/sota/text-classification-on-r8","task":"Text Classification","dataset":"R8","model":"ConvTextTM","rank_in_archive_order":20,"of":21,"metrics":{"Accuracy":"96.4"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}