No abstract

The world wide web is a mine of language data of unprecedented richness and ease of access (Kilgarriff and Grefenstette 2003). A growing body of studies has shown that simple algorithms using web-based evidence are successful at many linguistic tasks, often outperforming sophisticated methods based on smaller but more controlled data sources (cf. Turney 2001;Keller and Lapata 2003). Most current internet-based linguistic studies access the web through a commercial search engine. For example, some researchers rely on frequency estimates (number of hits) reported by engines (e.g. Turney 2001). Others use a search engine to find relevant pages, and then retrieve the pages to build a corpus (e.g. Ghani and Mladenic 2001; Baroni and Bernardini 2004).In

Error annotation is a key feature of modern learner corpora. Error identification is always based on some kind of reconstructed learner utterance (target hypothesis). Since a single target hypothesis can only cover a certain amount of linguistic information while ignoring other aspects, the need for multiple target hypotheses becomes apparent. Using the German learner corpus Falko as an example, we therefore argue for a flexible multi-layer stand-off corpus architecture where competing target hypotheses can be coded in parallel. Surface differences between the learner text and the target hypotheses can then be exploited for automatic error annotation.

No abstract

No abstract