Adaptive Information Extraction from Text by Rule Induction and Generalisation

(LP)2 is a covering algorithm for adaptive Information Extraction from text (IE). It induces symbolic rules that insert SGML tags into texts by learning from examples found in a userdefined tagged corpus. Training is performed in two steps: initially a set of tagging rules is learned; then additional rules are induced to correct mistakes and imprecision in tagging. Induction is performed by bottom-up generalization of examples in the training corpus. Shallow knowledge about Natural Language Processing (NLP) is used in the generalization process. The algorithm has a considerable success story. From a scientific point of view, experiments report excellent results with respect to the current state of the art on two publicly available corpora. From an application point of view, a successful industrial IE tool has been based on (LP)2 . Real world applications have been developed and licenses have been released to external companies for building other applications. This paper presents (LP)2...

• Type: Conference
• Year: 2001
• Where: IJCAI
• Authors: Fabio Ciravegna
• Keywords: Adaptive Information Extraction, Bottom-up Generalization, IJCAI 2001, IJCAI 2007, Successful Industrial Ie