Adaptive Information Extraction from Text by Rule Induction and Generalisation

By Fabio Ciravegna. International Joint Conference on Artificial Intelligence, 2001

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Towards the self-annotating web (2004, methodology): “Here, one encounters a vicious circle where there is no Semantic Web because of a lack of metadata, and there are no metadata, because there is no Semantic Web that one could learn from, for example, by training an IE system such as Amilcare ([7]).”
Improved Pattern Learning for Bootstrapped Entity Extraction (2014, methodology): “Other supervised systems like SRV (Freitag, 1998), SLIPPER (Cohen and Singer, 1999), ( LP ) 2 (Ciravegna, 2001), and RAPIER (Califf and Mooney, 1999) used a fully labeled corpus to either create or score rules.”
Composition of Conditional Random Fields for Transfer Learning (2005, unknown): “2 (LP) (Ciravegna, 2001) 99.”
S-CREAM: Semiautomatic CREAtion of Metadata (2002, methodology): “gorithm for supervised learning of IE rules based on Lazy-NLP [1] [3].”
The Semantic Grid: A Future e‐Science Infrastructure (2003, methodology): “A variety of information extraction tools and methods are being applied to the huge body of textual documents that are now available [Ciravegna01].”
MnM: Ontology Driven Semi-automatic and Automatic Support for Semantic Markup (2002, methodology): “The learning system is based on LP(2), a covering algorithm for supervised learning of IE rules based on Lazy-NLP ([3], [4]).”