Towards improving the precision of a relation extraction system by processing negation and speculation
We present preliminary results of a system that extracts biological relations from PubMed abstracts developed within the framework of the BIOGRAPH project. One of the text mining goals in the project is to develop techniques that allow to perform large scale relation extraction starting from the smallest possible amount of manually annotated data and obtaining the highest precision possible. In this poster we present experiments aimed at testing whether processing the scope of negation and speculation cues results in a higher precision of the relations extracted. Results show that the negation and speculation detection module increases the precision in 2.93% at the cost of decreasing recall in 0.68%.