DanProof: Pedagogical Spell and Grammar Checking for Danish

Proceedings of Recent Advances in Natural Language Processing, pages 55–62, Hissar, Bulgaria, Sep 7–9 2015. DanProof: Pedagogical Spell and Grammar Checking for Danish Eckhard Bick University of Southern Denmark eckhard.bick@mail.dk Abstract This paper presents a Constraint Grammar- based pedagogical proofing tool for Danish. The system recognizes not only spelling errors, but also grammatical errors in otherwise correctly spelled words, and categorizes errors for WORD-integrated pedagogical comments. Possible spelling corrections are prioritized from context, and grammatical corrections generated by a morphological module. The system uses both phonetic similarity measures and traditional Levenshtein-distances, and has a special focus on compounding/splitting errors common in modern Danish. As a classical spell-checker DanProof achieves F-Scores over 95, and F=88 if compounding correction is included. With the maximal set of error types, 2/3 of all errors are found in school essays, and precision is 91.7%. 1 Introduction Spell- and grammar-checking is not a new task, and is integrated in many standard text editors for the major languages. However, smaller languages are not so well covered, and the technology is very much inspired by what works for English where simple list checking will identify non-words, and correction suggestions can be found with the editing distance measure using the same list. However, the task is more difficult for morphologically rich languages, where word formation is too productive to allow lists with good coverage. A special problem for Danish is compounding, and standard, English- style spell checkers tempt users to (wrongly) split compounds into their parts just to satisfy their spell-checker. This phenomenon can now lead to a general tendency towards compounding errors in especially informal writing in Danish. Two other problems also deserve special attention: First, many errors are grammatical in nature rather than misspellings, and will lead to words that do exist in the spelling lexicon, an example being the confusion of finite and non- finite verb endings in Danish (købe - køber), which is considered a stigmatizing marker of low-level education. Detecting this error is only possible with context and true sentence analysis. Second, depending on the user group, it is not enough to come up with a loose list of similar words as correction suggestions - only good spellers will immediately see what the correct form is. Bad spellers need a well-prioritized list, or - if possible - just one suggestion, which is also desirable for tasks in automatic tool pipes, such as pre- and postprocessing of machine translation (Stymne & Ahrenberg 2010) or as an OCR module. To achieve such prioritization, simple editing distance is not enough. Rather, other factors, like phonetic similarity, compound- part similarity, frequency and not least context analysis, must be considered. While initiatives like hunspell and the use of finite state transducers (Pirinen & Lindén 2014; Antonsen 2014), have addressed the variability of morphologically rich languages, the use of full-scale grammatical and sentence analysis is rare. For the Scandinavian languages, the Constraint Grammar (CG) approach (Karlsson et al. 1995) has been used for this task (Arppe 2000; Birn 2000; Carlberger …

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