Authors:
Kristin Stamm
and
Andreas Dengel
Affiliation:
German Reseach Center for Artificial Intelligence and University Kaiserslautern, Germany
Keyword(s):
Machine Learning, Enterprise Applications, Evidence based Search, Information Gain Tree, Search Failure Detection, Degree of Belief, Dempster Shafer Theory, Multichannel Document Analysis.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Industrial Applications of AI
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
Abstract:
Enterprises today are challenged by managing requests arriving through all communication channels. To support service employees in better and faster understanding incoming documents, we developed the approach of process-driven document analysis (DA). We introduced the structure Attentive Task (AT) to formalize information expectations toward an incoming document. To map the documents to the corresponding AT, we previously developed a novel search approach that uses DA results as evidences for prioritizing all AT. With this approach, we consider numerous task instances including their context instead of a few process classes. The application of AT search in enterprises raises two challenges: (1) Complex domains require a structured selection of well performing evidence types, (2) a failure detection method is needed for handling a substantial part of incoming documents that cannot be related to any AT. Here, we apply methods from machine learning to meet these requirements. We learn a
nd apply information gain trees for structuring and optimizing evidence selection. We propose five strategies for detecting documents without ATs. We evaluate the suggested methods with two processes of a financial institution.
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