Second, when the number of target relations is small, and their names are known in advance, we show that O-CRF is able to match the precision of a traditional extraction system , though at substantially lower recall.

The unique nature of the open extraction task has led us to develop O-CRF, an open extraction system that uses the power of graphical models to identify relations in text.

The set of features used by O-CRF is largely similar to those used by O-NB and other state-of-the-art relation extraction systems , They include part-of-speech tags (predicted using a separately trained maximum-entropy model), regular expressions (e. g.detecting capitalization, punctuation, eta), context words, and conjunctions of features occurring in adjacent positions within six words to the left and six words to the right of the current word.

Our approach is to measure the impact of each parser when it is used as a component of an information extraction system that performs protein—protein interaction (PPI) identification in biomedical papers.

In our approach to parser evaluation, we measure the accuracy of a PPI extraction system , in which

In the following experiments, we used AImed (Bunescu and Mooney, 2004), which is a popular corpus for the evaluation of PPI extraction systems .