In the linguistic analysis of a digital natural language text, it is necessary to clearly define the characters, words, and sentences in any document. Defining these units presents different challenges depending on the language being processed and the source of the documents, and the task is not trivial, especially when considering the variety of human languages and writing systems. Natural languages contain inherent ambiguities, and writing systems often amplify ambiguities as well as generate additional ambiguities. Much of the challenge of Natural Language Processing (NLP) involves resolving these ambiguities. Early work in NLP focused on a small number of well-formed corpora in a small number of languages, but significant advances have beenmade in recent years by using large and diverse corpora from a wide range of sources, including a vast and ever-growing supply of dynamically generated text from the Internet. This explosion in corpus size and variety has necessitated techniques for automatically harvesting and preparing text corpora for NLP tasks.