Abstract
In this paper, we present a Conditional Random Fields (CRFs) framework for the Clause Splitting problem. We adapt the CRFs model to this problem in order to use very large sets of arbitrary, overlapping and non-independent features. We also extend N-best list by using the Joint-CRFs (Shi and Wang 2007). In addition, we propose the use of rich linguistic information along with a new bottom-up dynamic algorithm for decoding to split a sentence into clauses. The experiments show that our results are competitive with the state-of-the art results.
