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Natural Language in Business Process Models [electronic resource] : Theoretical Foundations, Techniques, and Applications / edited by Henrik Leopold.

Contributor(s): Leopold, Henrik [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Business Information Processing: 168Publisher: Cham : Springer International Publishing : Imprint: Springer, 2013Description: XXII, 181 p. 35 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319041759.Subject(s): Business | Information technology | Business -- Data processing | Computational linguistics | Application software | Business and Management | IT in Business | Language Translation and Linguistics | Information Systems Applications (incl. Internet) | Computational Linguistics | Computer Appl. in Administrative Data ProcessingAdditional physical formats: Printed edition:: No titleDDC classification: 650 | 658.05 Online resources: Click here to access online
Contents:
1 Business Process Management -- 2 Linguistics -- 3 Parsing and Annotating Process Model Elements -- 4 Detecting and Correcting Linguistic Guideline Violations -- 5 Generation of Natural Language Texts from Process Models -- 6 Service Derivation from Process Models -- 7 Conclusion.
In: Springer eBooksSummary: Natural language is one of the most important means of human communication. It enables us to express our will, to exchange thoughts, and to document our knowledge in written sources. Owing to its substantial role in many facets of human life, technology for automatically analyzing and processing natural language has recently become increasingly important. In fact, natural language processing tools have paved the way for entirely new business opportunities. The goal of this book is to facilitate the automatic analysis of natural language in process models and to employ this analysis for assisting process model stakeholders. Therefore, a technique is defined that automatically recognizes and annotates process model element labels. In addition, this technique is leveraged to support organizations in effectively utilizing their process models in various ways. The book is organized into seven chapters. It starts with an overview of business process management and linguistics and continues with conceptual contributions on parsing and annotating process model elements, with the detection and correction of process model guideline violations, with the generation of natural language from process models, and finally ends with the derivation of service candidates from process models.
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1 Business Process Management -- 2 Linguistics -- 3 Parsing and Annotating Process Model Elements -- 4 Detecting and Correcting Linguistic Guideline Violations -- 5 Generation of Natural Language Texts from Process Models -- 6 Service Derivation from Process Models -- 7 Conclusion.

Natural language is one of the most important means of human communication. It enables us to express our will, to exchange thoughts, and to document our knowledge in written sources. Owing to its substantial role in many facets of human life, technology for automatically analyzing and processing natural language has recently become increasingly important. In fact, natural language processing tools have paved the way for entirely new business opportunities. The goal of this book is to facilitate the automatic analysis of natural language in process models and to employ this analysis for assisting process model stakeholders. Therefore, a technique is defined that automatically recognizes and annotates process model element labels. In addition, this technique is leveraged to support organizations in effectively utilizing their process models in various ways. The book is organized into seven chapters. It starts with an overview of business process management and linguistics and continues with conceptual contributions on parsing and annotating process model elements, with the detection and correction of process model guideline violations, with the generation of natural language from process models, and finally ends with the derivation of service candidates from process models.

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