In designing data structures for text databases, it is valuable to know how many different words are likely to be encountered in a particular collection. For example, vocabulary accumulation is central to index construction for text database systems; it is useful to be able to estimate the space requirements and performance characteristics of the main-memory data structures used for this task. However, it is not clear how many distinct words will be found in a text collection or whether new words will continue to appear after inspecting large volumes of data. We propose practical definitions of a word and investigate new word occurrences under these models in a large text collection. We inspected around two billion word occurrences in 45 GB of World Wide Web documents and found just over 9.74 million different words in 5.5 million documents; overall, 1 word in 200 was new. We observe that new words continue to occur, even in very large datasets, and that choosing stricter definitions of what constitutes a word has only limited impact on the number of new words found.