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        <contrib contrib-type="author">
          <string-name>Wei-Fan Chen Lutz Schröder</string-name>
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        <contrib contrib-type="author">
          <string-name>Philipp Heinisch Adrian Ulges</string-name>
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          <institution>Organizers: Mirko Lenz (University of Trier, Germany) Lorik Dumani (University of Trier, Germany) Premtim Sahitaj (University of Trier, Germany) Jordan Robinson, University of Liverpool</institution>
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          <country country="UK">United Kingdom</country>
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      <abstract>
        <p>Digital text data is produced across diferent sources such as social media. Simultaneously, very often only structured data is available. Within CBR, cases of the former are usually handled by using methods of Textual CBR, while Process-Oriented CBR addresses on the latter type of data. By leveraging their generic research origins, i.e., text mining and text generation approaches, we aim to diminish this gap. The target of text mining is to extract (useful) structured information from unstructured text. In contrast, text generation attempts to (automatically) create text from structured information or distributed knowledge. The goal of the TMG workshop is to bring these two perspectives together by eliciting research paper submissions that aim for applying text mining and generation approach in the context of CBR. We welcome any submission from any domain aiming to contribute to close this gap.</p>
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