SAPKOS: Experimental Czech Multi-label Document Classification and Analysis System
作者： Ladislav Lenc , Pavel Král
DOI: 10.1007/978-3-319-23868-5_24
关键词: Principle of maximum entropy 、 Czech 、 Task (project management) 、 Computer science 、 Machine learning 、 Systems architecture 、 Measure (data warehouse) 、 Artificial intelligence 、 Document classification 、 Annotation 、 Information retrieval 、 Newspaper
摘要: This paper presents an experimental multi-label document classification and analysis system called SAPKOS. The which integrates the state-of-the-art machine learning natural language processing approaches is intended to be used by Czech news Agency (CTK). Its main purpose save human resources in task of annotation newspaper articles with topics. Another important functionality automatic comparison CTK production popular media. results this will adapt better correspond today’s market requirements. An interesting contribution that, best our knowledge, no other exists. It also worth mentioning that accuracy very high. score obtained due unique architecture a maximum entropy based engine novel confidence measure method.
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