Wiktionary is a large-scale resource for cross-lingual lexical information with great potential utility for machine translation (MT) and many other NLP tasks, especially automatic morphological analysis and generation. However, it is designed primarily for human viewing rather than machine readability, and presents numerous challenges for generalized parsing and extraction due to a lack of standardized formatting and grammatical descriptor definitions. This paper describes a large-scale effort to automatically extract and standardize the data in Wiktionary and make it available for use by the NLP research community. The methodological innovations include a multidimensional table parsing algorithm, a cross-lexeme, token-frequency-based method of separating inflectional form data from grammatical descriptors, the normalization of grammatical descriptors to a unified annotation scheme that accounts for cross-linguistic diversity, and a verification and correction process that exploits within-language, cross-lexeme table format consistency to minimize human effort. The effort described here resulted in the extraction of a uniquely large normalized resource of nearly 1,000,000 inflectional paradigms across 350 languages. Evaluation shows that even though the data is extracted using a language-independent approach, it is comparable in quantity and quality to data extracted using hand-tuned, language-specific approaches.