  Collaborative work on unstructured or semi-structured documents, such as in
literature corpora or source code, often involves agreed upon templates
containing metadata. These templates are not consistent across users and over
time. Rule-based parsing of these templates is expensive to maintain and tends
to fail as new documents are added. Statistical techniques based on frequent
occurrences have the potential to identify automatically a large fraction of
the templates, thus reducing the burden on the programmers. We investigate the
case of the Project Gutenberg corpus, where most documents are in ASCII format
with preambles and epilogues that are often copied and pasted or manually
typed. We show that a statistical approach can solve most cases though some
documents require knowledge of English. We also survey various technical
solutions that make our approach applicable to large data sets.
