We thus propose to model the reranking phase (RR) as a HMM sequence labeling task.

the estimation of lexico-grammatical preferences through distributional analysis over unlabeled data), estimation (through syntactic or lexical backoff where necessary) and reranking .

In these experiments we evaluate the quality of the argument classification step against the lexical knowledge acquired from unlabeled texts and the reranking step.

The Global Prior model is obtained by applying reranking (Section 3.2) to the best n = 10 candidates provided by the Local Prior model.

6) and the HMM-based reranking characterize the final two configurations.

This approach effectively introduces a new step in SRL, also called Joint Reranking , (RR), e.g.

approach is to rerank the results from stage 1, instead of doing actual binary classification.

6.1 Reranking using a lexicon

To rerank a list of posts retrieved for a given topic, we opt to use the method that showed best performance at TREC 2008.

In stage (2) one commonly uses either a binary classifier to distinguish between opinionated and non-opinionated documents or applies reranking of the initial result list using some opinion score.

The best performing opinion finding system at TREC 2008 is a two-stage approach using reranking in stage (2) (Lee et al., 2008).

This opinion score is combined with the relevance score, and posts are reranked according to this new score.