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More than Words: Using Token Context to Improve Canonicalization of Historical German

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 fixed lexicon accessed by orthographic form, such as information retrieval systems (Sokirko, 2003; Cafarella and Cutting, 2004), part-of-speech taggers (DeRose, 1988; Brill, 1992; Schmid, 1994), simple word stemmers (Lovins, 1968; Porter, 1980), or more sophisticated morphological analyzers (Geyken and Hanneforth, 2006; Zielinski et al., 2009).1 Traditional approaches to the problems arising from an attempt to incorporate historical text into such a system rely on the use of additional specialized (often application-specific) lexical resources to explicitly encode known historical variants. Such specialized lexica are not only costly and time-consuming to create, but also – in their simplest form of static finite word lists – necessarily incomplete in the case of a morphologically productive language like German, since a simple finite lexicon cannot account for highly productive morphological processes such as nominal composition (cf. Kempken et al., 2006). To facilitate the extension of synchronically-oriented natural language processing techniques to historical text while minimizing the need for specialized lexical resources, one may first attempt an automatic canonicalization of the input text. Canonicalization approaches (Jurish, 2008, 2010a; Gotscharek et al., 2009a) treat orthographic variation phenomena in historical text as instances of an error-correction problem (Shannon, 1948; Kukich, 1992; Brill and Moore, 2000), seeking to map each (unknown) word of the input text to one or more extant canonical cognates: synchronically active types which preserve both the root and morphosyntactic features of the associated historical form(s). To the extent that the canonicalization was successful, application-specific processing can then proceed normally using the returned canonical forms as input, without any need for additional modifications to the application lexicon. I distinguish between type-wise canonicalization techniques which process each input word independently and token-wise techniques which make use of the context in which a given instance of a word occurs. In this paper, I present a token-wise canonicalization