A hybrid approach for extracting semantic relations from texts

We present an approach for extracting relations from texts that exploits linguistic and empirical strategies, by means of a pipeline method involving a parser, partof-speech tagger, named entity recognition system, pattern-based classification and word sense disambiguation models, and resources such as ontology, knowledge base and lexical databases. The relations extracted can be used for various tasks, including semantic web annotation and ontology learning. We suggest that the use of knowledge intensive strategies to process the input text and corpusbased techniques to deal with unpredicted cases and ambiguity problems allows to accurately discover the relevant relations between pairs of entities in that text.