{"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/distributed-word-representation-in-tsetlin","title":"Enhancing Interpretable Clauses Semantically using Pretrained Word Representation","arxiv_id":"2104.06901","date":"2021-04-14","proceeding":"EMNLP (BlackboxNLP) 2021 11","authors":["Rohan Kumar Yadav","Lei Jiao","Ole-Christoffer Granmo","Morten Goodwin"],"abstract":"Tsetlin Machine (TM) is an interpretable pattern recognition algorithm based on propositional logic, which has demonstrated competitive performance in many Natural Language Processing (NLP) tasks, including sentiment analysis, text classification, and Word Sense Disambiguation. To obtain human-level interpretability, legacy TM employs Boolean input features such as bag-of-words (BOW). However, the BOW representation makes it difficult to use any pre-trained information, for instance, word2vec and GloVe word representations. This restriction has constrained the performance of TM compared to deep neural networks (DNNs) in NLP. To reduce the performance gap, in this paper, we propose a novel way of using pre-trained word representations for TM. The approach significantly enhances the performance and interpretability of TM. We achieve this by extracting semantically related words from pre-trained word representations as input features to the TM. Our experiments show that the accuracy of the proposed approach is significantly higher than the previous BOW-based TM, reaching the level of DNN-based models.","url_abs":"https://arxiv.org/abs/2104.06901v2","url_pdf":"https://arxiv.org/pdf/2104.06901v2.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":"distributed-word-representation-in-tsetlin","repo_url":"https://github.com/cair/PyTsetlinMachineCUDA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"distributed-word-representation-in-tsetlin","repo_url":"https://github.com/cair/TsetlinMachine","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"distributed-word-representation-in-tsetlin","repo_url":"https://github.com/cair/pyTsetlinMachine","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"distributed-word-representation-in-tsetlin","repo_url":"https://github.com/cair/pyTsetlinMachineMT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"distributed-word-representation-in-tsetlin","repo_url":"https://github.com/cair/pyTsetlinMachineParallel","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"distributed-word-representation-in-tsetlin","repo_url":"https://github.com/ckinateder/pytsetlinmachineparallel","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"word-sense-disambiguation","task_name":"Word Sense Disambiguation"}],"methods":[{"method_slug":"glove","method_name":"GloVe"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sentiment-analysis-on-mr","task":"Sentiment Analysis","dataset":"MR","model":"TM-Glove","rank_in_archive_order":13,"of":19,"metrics":{"Accuracy":"77.51"},"uses_additional_data":false},{"leaderboard":"/sota/text-classification-on-r52","task":"Text Classification","dataset":"R52","model":"TM-Glove","rank_in_archive_order":8,"of":8,"metrics":{"Accuracy":"89.14"},"uses_additional_data":false},{"leaderboard":"/sota/text-classification-on-r8","task":"Text Classification","dataset":"R8","model":"TM-Glove","rank_in_archive_order":11,"of":21,"metrics":{"Accuracy":"97.50"},"uses_additional_data":false},{"leaderboard":"/sota/text-classification-on-trec-6","task":"Text Classification","dataset":"TREC-6","model":"TM-Glove","rank_in_archive_order":19,"of":19,"metrics":{"Error":"9.96"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}