Automatic Relation Extraction for Building Smart City Ecosystems using Dependency Parsing
Understanding and analysing rapidly changing and growing business ecosystems, like smart city and mobility ecosystems, becomes increasingly difficult. However, the understanding of these ecosystems is the key to being successful for all involved parties, like companies and public institutions. Modern Natural Language Processing technologies can help to automatically identify and extract relevant information from sources like online news and blog articles and hence support the analysis of complex ecosystems. In this paper, we present an approach to automatically extract directed relations between entities within business ecosystems from online news and blog articles by using dependency parsing.
| Attribute | Value |
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| Address | |
| Authors | Dr. Daniel Braun , Dr. Anne Faber , Adrian Hernandez-Mendez , Prof. Dr. Florian Matthes |
| Citation | Braun, D.; Faber, A.; Hernandez A., Adrian; Matthes, F.: Automatic Relation Extraction for Building Smart City Ecosystems using Dependency Parsing. Proceedings of the 2nd Workshop on Natural Language for Artificial Intelligence (NL4AI), Trento, Italy, 2018 |
| Key | Br18e |
| Research project | Business Ecosystem Modeling and Visualization , Meta Model based Natural Language Generation for Automatic Abstractive Text Summarization (A-SUM) , Privacy-Enhancing Technologies |
| Title | Automatic Relation Extraction for Building Smart City Ecosystems using Dependency Parsing |
| Type of publication | Workshop |
| Year | 2018 |
| Project | Meta Model based Natural Language Generation for Automatic Abstractive Text Summarization (A-SUM) , Business Ecosystem Modeling and Visualization |
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