| MWE-dedicated Features | In order to make these models comparable, we use two comparable sets of feature templates: one adapted to sequence labelling (CRF—based MWER) and the other one adapted to reranking (MaXEnt-based reranker). |
| Two strategies, two discriminative models | MWER can be seen as a sequence labelling task (like chunking) by using an IOB-like annotation scheme (Ramshaw and Marcus, 1995). |
| Two strategies, two discriminative models | Constant and Sigogne (2011) proposed to combine MWE segmentation and part-of-speech tagging into a single sequence labelling task by assigning to each token a tag of the form TAG+X where TAG is the part-of-speech (POS) of the leXical unit the token belongs to and X is either B (i.e. |
| Two strategies, two discriminative models | (2001) for sequential labelling . |
| Thread Structure Tagging | To automatically label sentences in a thread with their types, we adopt a sequence labeling approach, specifically linear-chain conditional random fields (CRFs), which have shown good performance in many other tasks (Lafferty, 2001). |
| Thread Structure Tagging | In the sequence labeling task, feature functions across the sequence are often tied together. |
| Thread Structure Tagging | In order to take advantage of such adjacent sentence dependency, we use the linear-chain CRFs for the sequence labeling . |