We introduce the unsupervised text annotation model UTA, which iteratively populates a document-specific database containing the related symbolic content description. The model identifies the most related documents using the text of documents and the symbolic content description. UTA extends the database of one document with data from related documents without ignoring the precision.
|Title of host publication||Proceedings of the 6th Workshop on Dynamics of Knowledge and Belief (DKB-2017) and the 5th Workshop KI Kognition (KIK-2017) co-located with 40th German Conference on Artificial Intelligence (KI 2017)|
|Number of pages||8|
|Publication status||Published - 01.09.2017|
|Event||6th Workshop on Dynamics of Knowledge and Belief and the 5th Workshop KI and Kognition - Dortmund, Germany|
Duration: 26.09.2017 → 26.09.2017
Conference number: 130603