{"slug": "natural-language-processing-almost-from-scratch", "title": "Natural Language Processing Almost from Scratch", "summary": "Researchers proposed a unified neural network architecture and learning algorithm for natural language processing tasks including part-of-speech tagging, chunking, named entity recognition, and semantic role labeling, published on arXiv on March 2, 2011. The system learns internal representations from vast amounts of mostly unlabeled data, avoiding task-specific engineering, and serves as a basis for a freely available tagging system with minimal computational requirements.", "body_md": "# Computer Science > Machine Learning\n\n[Submitted on 2 Mar 2011]\n\n# Title:Natural Language Processing (almost) from Scratch\n\n[View PDF](/pdf/1103.0398)\n\n[HTML (experimental)](https://arxiv.org/html/1103.0398v1)\n\nAbstract:We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid task-specific engineering and therefore disregarding a lot of prior knowledge. Instead of exploiting man-made input features carefully optimized for each task, our system learns internal representations on the basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for building a freely available tagging system with good performance and minimal computational requirements.\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\nIArxiv Recommender\n\n*(*[What is IArxiv?](https://iarxiv.org/about))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/natural-language-processing-almost-from-scratch", "canonical_source": "https://arxiv.org/abs/1103.0398", "published_at": "2026-08-12 18:31:31+00:00", "updated_at": "2026-08-12 18:42:28.882657+00:00", "lang": "en", "topics": ["natural-language-processing", "machine-learning", "neural-networks"], "entities": ["arXiv"], "alternates": {"html": "https://wpnews.pro/news/natural-language-processing-almost-from-scratch", "markdown": "https://wpnews.pro/news/natural-language-processing-almost-from-scratch.md", "text": "https://wpnews.pro/news/natural-language-processing-almost-from-scratch.txt", "jsonld": "https://wpnews.pro/news/natural-language-processing-almost-from-scratch.jsonld"}}