How Symbolic Learning Can Help Statistical Learning (and vice versa)

We describe in this paper how different learning strategies can be applied on the same NLP task, namely chunking. The reference corpus is extracted from the French Treebank, the symbolic learning strategy used is grammatical inference and the statistical one is CRFs (Conditional Random Fields). As expected, the symbolic approach allows readability but is less effective than the statistical one. We then propose two distinct ways to combine both approaches and show that in both cases they benefit from one another.