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SCALABLE DATA ANALYTICS
AND ARTIFICIAL INTELLIGENCE

Martin Potthast  - Technologies for Information Retrieval and Summarization

 

Topic: 

An argument is a text span composed of a claim that is supported by reasons. In recent years, mining arguments from text has become a hot topic in natural language processing. As the technology to identify arguments in text matures, larger and larger datasets of arguments on a wide variety of topics have been compiled. In order to facilitate their use in practical applications, such as an argument search engine, tailored approaches to the retrieval of arguments and their summarization play a key role. This talk discusses a number of basic and applied technologies for information retrieval and summarization with applications to the retrieval of arguments.

Bio

Martin Potthast is head of the Text Mining and Retrieval Group at Leipzig University. His research focuses on information processing, the challenges arising from assessing the credibility and originality of information obtained online, and the development of information systems. Martin Potthast has contributed to the fields of information retrieval, natural language processing, and data science. Several of his achievements have been awarded with scientific prizes.

Professional background: Martin Potthast studied computer science at Paderborn University (2001-2006), completed his dissertation at the Bauhaus-Universität Weimar in December 2011, where he also spent his postdoctoral research at the Digital Bauhaus Lab. Martin was appointed Junior-Professor (tenure track) at Leipzig University in October 2017.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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