NDLI: Ensemble learning for keyword extraction from event descriptions

Content Provider	IEEE Xplore Digital Library
Author	Geadas, P. Alves, A. Ribeiro, B.
Copyright Year	2014
Description	Author affiliation: Polytech. Inst. of Coimbra (IPC), Centre for Inf. & Syst. (CISUC), Univ. of Coimbra, Coimbra, Portugal (Alves, A.) \|\| Center of Inf. & Syst., Univ. of Coimbra, Coimbra, Portugal (Geadas, P.) \|\| Dept. of Inf. Eng., Univ. of Coimbra, Coimbra, Portugal (Ribeiro, B.)
Abstract	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.
Starting Page	2669
Ending Page	2676
File Size	608928
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889911
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Abstracts Data mining Neural networks Pragmatics Training Focusing
Content Type	Text
Resource Type	Article

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