Text-Item Classification Software System
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A Text-Item Classification Software System is a classification software system that can solve a text-item classification task (by implementing a text-item classification algorithm).
- Context:
- It can range from being a Heuristic Text Classification System to being a Data-Driven Text Classification System (such as a supervised text classifier).
- It can range from being a Single-Label Text Classification System to being (typically) a Multi-Label Text Classification System.
- It can utilize various natural language processing (NLP) techniques to preprocess, vectorize, and classify text-items.
- It can be designed to work with texts of various sizes, from short texts like tweets and product reviews to longer documents such as articles and legal documents.
- It can employ different machine learning models, including decision trees, neural networks, and support vector machines, depending on the complexity of the task and the nature of the text.
- It can be applied in numerous domains, including but not limited to spam detection, sentiment analysis, topic classification, and document organization.
- Example(s):
- a News Categorization System, such as: Google News Service.
- a Research Paper Classification System, such as: arXiv's Automated Classification.
- a Social Media Sentiment Analysis System, such as: Twitter Sentiment Analysis Tools.
- a Document Classification System (for document classification task).
- a Contract Article Classification System (for contract article classification task).
- a Document Classification System (for document classification task)s, such as a commercial contract categorization system.
- a Contract Article Classification System (for contract article classification task).
- …
- Counter-Example(s):
- See: NLP System.
References
2009
- NLSR Service http://registry.dfki.de/sections.php3?f_mainsection=2&f_section=50
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- weta: Weta is an open source* framework for text analysis implemented in Java.
- WMTrans Products: text processing software for various NLP applications