Constructing a Canonicalized Corpus of Historical German by Text Alignment ---draft

Historical text presents numerous challenges for contemporary natural language processing techniques. In particular, the absence of consistent orthographic conventions in historical text presents difficulties for any system requiring reference to a static lexicon indexed by orthographic form. Canonicalization approaches seek to address these issues by assigning an extant equivalent to each word of the input text and deferring application analysis to these canonical forms. Quantitative evaluation of canonicalization techniques in terms of precision and recall requires reference to a ground-truth corpus in which the canonical form for each corpus token has been manually verified, but such manually annotated corpora are difficult to come by and in general both costly and time-consuming to create. In this paper, we describe a method for bootstrapping a ground-truth canonicalization corpus with minimal manual annotation effort by means of automatic alignment of historical texts with current editions of the same texts, coupled with a two-phase manual review process.