Information extraction systems process natural language documents and locate a specific set of relevant items. Given the recent success of empirical or corpusbased approaches in other areas of natural language processing, machine learning has the potential to significantly aid the development of these knowledge-intensive systems. This paper presents a system, RAPmrt, that takes pairs of documents and filled templates and induces pattern-match rules that directly extract fillers for the slots in the template. The learning algorithm incorporates techniques from several inductive logic programming systems and learns unbounded patterns that include constraints on the words and partof-speech tags surrounding the filler. Encouraging results are presented on learning to extract information from computer job postings from the newsgroup misc. jobs. offered.
Rapier REF ) is a supervised extraction system that uses a relational learning algorithm to extract information from job postings.
Relational Learning Of Pattern-Match Rules For Information Extraction
Workshop On Computational Natural Language Learning CoNLL
Computer Science