In order to find out whether IS categories are unevenly distributed within German sentences we examine a corpus of German radio news bulletins that has been manually annotated for IS (496 annotated sentences in total) using the scheme of Riester (2008b).5

(2007) present work on predicting the dative alternation in English using 14 features relating to information status which were manually annotated in their corpus.

In our work, we manually annotate a small corpus in order to learn generalisations.

From these we learn features that approximate the generalisations, enabling us to apply them to large amounts of unseen data without further manual annotation .

The problem, of course, is that we do not possess any reliable system of automatically assigning IS labels to unknown text and manual annotations are costly and time-consuming.

If Cflyj) exceeds a certain confidence threshold t, is assumed to be the correct label for this token and assigned to it.2 Otherwise, manual annotation of this token is required.

So, using SeSAL the complete corpus can be labeled with only a small fraction of it actually being manually annotated (MUC7: about 18 %, PENNBIOIE: about 13 %).

In most annotation campaigns, the language material chosen for manual annotation is selected randomly from some reference corpus.

In the AL paradigm, only examples of high training utility are selected for manual annotation in an iterative manner.

Our experiments in the context of the NER scenario render evidence to the hypothesis that the proposed approach to semi-supervised AL (SeSAL) for sequence labeling indeed strongly reduces the amount of tokens to be manually annotated — in terms of numbers, about 60% compared to its fully supervised counterpart (FuSAL), and over 80% compared to a totally passive learning scheme based on random selection.

Both S and L classifiers are trained using manually annotated documents taken from the RST—DT corpus.

In training mode, classification instances are built by parsing manually annotated trees from the RST—DT corpus paired with lexicalized syntax trees (LS Trees) for each sentence (see Sect.

In this paper, we have shown that it is possible to build an accurate automatic text-level discourse parser based on supervised machine-learning algorithms, using a feature-driven approach and a manually annotated corpus.

A measure of our full system’s performance is realized by comparing structure and labeling of the RST tree produced by our algorithm to that obtained through manual annotation (our gold standard).

All but the latter three were then characterised in terms of features manually annotated in the Penn Discourse TreeBank — discourse connectives and their senses.

It has characterised each genre in terms of features manually annotated in the Penn Discourse TreeBank, and used this to show that genre should be made a factor in automated sense labelling of discourse relations that are not explicitly marked.

Genre differences at the level of discourse in the PTB can be seen in the manual annotations of the Penn Discourse TreeBank (Prasad et al., 2008).

These have been manually annotated using the three-level sense hierarchy described in detail in (Miltsakaki et al., 2008).