{"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/on-the-role-of-text-preprocessing-in-neural","title":"On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysis","arxiv_id":"1707.01780","date":"2017-07-06","proceeding":"WS 2018 11","authors":["Jose Camacho-Collados","Mohammad Taher Pilehvar"],"abstract":"Text preprocessing is often the first step in the pipeline of a Natural\nLanguage Processing (NLP) system, with potential impact in its final\nperformance. Despite its importance, text preprocessing has not received much\nattention in the deep learning literature. In this paper we investigate the\nimpact of simple text preprocessing decisions (particularly tokenizing,\nlemmatizing, lowercasing and multiword grouping) on the performance of a\nstandard neural text classifier. We perform an extensive evaluation on standard\nbenchmarks from text categorization and sentiment analysis. While our\nexperiments show that a simple tokenization of input text is generally\nadequate, they also highlight significant degrees of variability across\npreprocessing techniques. This reveals the importance of paying attention to\nthis usually-overlooked step in the pipeline, particularly when comparing\ndifferent models. Finally, our evaluation provides insights into the best\npreprocessing practices for training word embeddings.","url_abs":"http://arxiv.org/abs/1707.01780v3","url_pdf":"http://arxiv.org/pdf/1707.01780v3.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":"on-the-role-of-text-preprocessing-in-neural","repo_url":"https://github.com/pedrada88/preproc-textclassification","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"on-the-role-of-text-preprocessing-in-neural","repo_url":"https://github.com/changji2069/Scope-Project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"on-the-role-of-text-preprocessing-in-neural","repo_url":"https://github.com/changji2069/literature-review","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"text-categorization","task_name":"Text Categorization"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sentiment-analysis-on-imdb","task":"Sentiment Analysis","dataset":"IMDb","model":"CNN+LSTM","rank_in_archive_order":38,"of":49,"metrics":{"Accuracy":"88.9"},"uses_additional_data":true},{"leaderboard":"/sota/sentiment-analysis-on-sst-2-binary","task":"Sentiment Analysis","dataset":"SST-2 Binary classification","model":"CNN","rank_in_archive_order":56,"of":87,"metrics":{"Accuracy":"91.2"},"uses_additional_data":false},{"leaderboard":"/sota/text-classification-on-ohsumed","task":"Text Classification","dataset":"Ohsumed","model":"CNN+Lowercased","rank_in_archive_order":10,"of":10,"metrics":{"Accuracy":"36.2"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}