Ensemble Learning for Keyword Extraction from Event Descriptions



Automatic keyword extraction (AKE) from textual sources took a valuable step towards harnessing the problem of efficient scanning of large document collections. Particularly in the context of urban mobility, where the most relevant events in the city are advertised on-line, it becomes difficult to know exactly what is happening in a place. In this paper we tackle this problem by extracting a set of keywords from different kinds of textual sources, focusing on the urban events context. We propose an ensemble of automatic keyword extraction systems KEA (Keyphrase Extraction Algorithm) and KUSCO (Knowledge Unsupervised Search for instantiating Concepts on lightweight Ontologies) and Conditional Random Fields (CRF). Unlike KEA and KUSCO which are well-known tools for automatic keyword extraction, CRF needs further preprocessing. Therefore, a tool for handling AKE from the documents using CRF is developed.
The architecture for the AKE ensemble system is designed and efficient integration of component applications is achieved. Finally, we empirically show that our AKE ensemble system significantly succeeds on baseline sources and urban events collections.


Ensemble Systems, Machine Learning, Keyword Extraction

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IEEE International Joint Conference on Neural Networks (IJCNN), July 2014, July 2014


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