We also analyze the difficulty of extending traditional sentence-level discourse parsing to text-level parsing by comparing discourse-parsing performance under different discourse conditions.

We analyzed the difficulty of extending traditional sentence-level discourse parsing to text-level parsing by showing that using exactly the same set of features, the performance of Structure and Relation classification on cross-sentence instances is consistently inferior to that on within-sentence instances.

difficulty with extending traditional sentence-level discourse parsing to text-level parsing, by comparing discourse parsing performance under different discourse conditions.

Unlike syntactic parsing, where we are almost never interested in parsing above sentence level, sentence-level parsing is not sufficient for discourse parsing.

While a sequence of local ( sentence-level ) grammaticality can be considered to be global grammaticality, a sequence of local discourse coherence does not necessarily form a globally coherent text.

Text-level discourse parsing imposes more constraints on the global coherence than sentence-level discourse parsing.

which just utilize the independent sentence-level features, behave not vary well here, and there is no statistically significant performance difference between them.

To see how much the different textual and non-textual features contribute to community answer summarization, the accumulated weight of each group of sentence-level features5 is presented in Figure 2.

In this section, we give a detailed description of the different sentence-level cQA features and the contextual modeling between sentences used in our model for answer summarization.

Sentence-level Features

These sentence-level features can be easily utilized in the CRF framework.

We proposed a novel backoff modeling of an SR-TSG based on the hierarchical Pitman-Yor Process and sentence-level and tree-level blocked MCMC sampling for training our model.

In each splitting step, we use two types of blocked MCMC algorithm: the sentence-level blocked Metroporil-Hastings (MH) sampler and the tree-level blocked Gibbs sampler, while (Petrov et al., 2006) use a different MLE-based model and the EM algorithm.

Our sampler iterates sentence-level sampling and tree-level sampling alternately.

The sentence-level MH sampler is a recently proposed algorithm for grammar induction (Johnson et al., 2007b; Cohn et al., 2010).

In the updating formula, we need to compute the sentence-level BLE U (En, E5).

a non-clipped BP, BP = 60—9, for sentence-level BLEU].

sentence-level BLEU (Exp.