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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 6/2013

01.11.2013 | Original Article

SREC: Discourse-level semantic relation extraction from text

verfasst von: Mohammad-hadi Zahedi, Mohsen Kahani

Erschienen in: Neural Computing and Applications | Ausgabe 6/2013

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Abstract

Semantic relation extraction is a significant topic in semantic web and natural language processing with various important applications such as knowledge acquisition, web and text mining, information retrieval and search engine, text classification and summarization. Many approaches such rule base, machine learning and statistical methods have been applied, targeting different types of relation ranging from hyponymy, hypernymy, meronymy, holonymy to domain-specific relation. In this paper, we present a computational method for extraction of explicit and implicit semantic relation from text, by applying statistic and linear algebraic approaches besides syntactic and semantic processing of text.
Vorheriger Artikel Robustness analysis of global exponential stability of neural networks with Markovian switching in the presence of time-varying delays or noises
Nächster Artikel Prediction of fetal state from the cardiotocogram recordings using adaptive neuro-fuzzy inference systems

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Fußnoten
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Metadaten
Titel
SREC: Discourse-level semantic relation extraction from text
verfasst von
Mohammad-hadi Zahedi
Mohsen Kahani
Publikationsdatum
01.11.2013
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 6/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-1109-9

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