Identification of multi-word expressions by combining multiple linguistic information sources

We propose an architecture for expressing various linguistically-motivated features that help identify multi-word expressions in natural language texts. The architecture combines various linguistically-motivated classification features in a Bayesian Network. We introduce novel ways for computing many of these features, and manually define linguistically-motivated interrelationships among them, which the Bayesian network models. Our methodology is almost entirely unsupervised and completely language in dependent; it relies on few language resources and is thus suitable for a large number of languages. Furthermore, unlike much recent work, our approach can identify expressions of various types and syntactic constructions. We demonstrate a significant improvement in identification accuracy, compared with less sophisticated baselines.