Dependency-Based Text Compression for Semantic Relation Extraction

The application of linguistic patterns and rules are one of the main approaches for Information Extraction as well as for highquality ontology population. However, the lack of flexibility of the linguistic patterns often causes low coverage. This paper presents a weakly-supervised rule-based approach for Relation Extraction which performs partial dependency parsing in order to simplify the linguistic structure of a sentence. This simplification allows us to apply generic semantic extraction rules, obtained with a distantsupervision strategy which takes advantage of semi-structured resources. The rules are added to a partial dependency grammar, which is compiled into a parser capable of extracting instances of the desired relations. Experiments in different Spanish and Portuguese corpora show that this method maintains the highprecision values of rule-based approaches while improves the recall of these systems.