Due to the influx of technical terminology, the globalizing effects of the Internet, and to the natural processes of language change, even the most reluctant languages are currently being infused with significant numbers of new words. But since the search for neologisms is a very time consuming and labour intensive process, automation of the search activity is a highly desirable goal. Our aim is to identify an efficient and accurate automated method for identifying neologisms in an open corpus. Baayen and Lieber (p 801) suggest that neologisms tend to occur as hapax legomena in corpora. We explore this idea in the hope of making it more general through the use of relative token frequencies. The second part of the research attempts to deduce what kinds of productive processes are involved in word production and to identify productive affixes in the retrieved neologisms. The program which performs all these tasks has been aptly named NeoloSearch. The analysis consists of four fully automated phases. In the first phase the corpus is created from documents collected over the Internet via a specially developed tool. Because of the difficulties in representing and processing typographic marks like accents, fewer electronic corpora studies have been conducted on a language such as French. In response to this trend, French was used as the basis of our study, though the method should be easily adaptable to other languages. The second phase performs a statistical analysis on the tokens contained in the corpus in an attempt to identify neologisms. The third phase weeds out rare words, proper names, and typographic errors in the neologism candidate list. Finally, the context of each token in the list is analysed for additional information and common affixes are identified. The Internet provides a wealth of documents online which can potentially be used as the basis of a corpus. A fully automated Internet 'robot' was constructed to speed up the acquisition of documents utilizing a breadth-first-search algorithm. Retrieved documents are validated according to a predetermined content heuristic: documents must be of a specified size and be of the correct language. A simple counting method is used to observe the percentage of tokens which also appear in an online corpus of inflected forms. Any indecipherable tokens, for instance those that contain numerals, are thrown away and accents are converted to a standard format. Baayen and Lieber (p 809) demonstrated that it is possible in several ways to quantify a measurement of the productivity of an affix with respect to the productivity of other affixes found in the corpora. The rate at which new lexemes are created indicates an affix's productivity. If the object of interest is not the affix but the resultant lexeme, then it follows from their work (and from common intuition) that while common forms will occur in high frequency, neologisms will tend to occur with very low frequency. The first phase of the analysis involves calculating the frequency of each token in the corpora. These values are then tabulated to yield a table of frequencies of token frequencies. The mean, standard deviation and z-scores for each occurrence frequency are calculated and tabulated. Only tokens whose z-scores fall below an arbitrary cut-off point are selected for study. The conversion to standard units allows the threshold principle to be generalized to any size corpus. If the corpus is of a minimum size, there is a high probability that the only tokens occurring in the reduced set will be proper names, rare words, typographic errors and neologisms. Consequently, the tokens that fall within the selected group are looked up in a corpus of exclusion which contains common inflected forms and proper names. Any forms found in the corpus of exclusion are thrown away. Then a filter which uses approximate string matching techniques attempts to eliminate any tokens which are the result of typographic errors by attempting to match the tokens with their closest match in the corpus of exclusion. To study the production of new words more than just a list of anticipated neologisms is needed. Consequently, the tokens which pass through all the above stages are subjected to a simple parsing analysis which attempts to deduce word type and proximity to productive verbs using a +/- n-word context. Observations are summed and reported by word type and context and concordances are observed. Finally, string matching techniques are employed to identify commonly occurring affixes in the list of remaining tokens. This helps the researcher attempt to deduce the sources of the neologisms and what types of productive forces are at work. Statistics on all phases of the analysis are also part of the program output at the end of the run and a +/-1 sentence contexts can be generated for each neologism retrieved from the corpus using a simple query. There are several important discussions which result from this work. World Wide Web documents rest somewhere in permanence between spoken language and printed media and represent a unique means of communication. There are many mass-media characteristics to the Internet, with millions of subscribers world-wide and a multitude of on-line resources, including electronic journals and newspapers. It would be useful to contrast results of this experiment with those resulting from a closed corpus consisting of a more conventional mixture of spoken language and printed media. The statistical methods employed make the identification of collocations, semantic neologisms, and some compound words improbable and future research is needed to explore these areas. The user, however, can give feedback to the program after each analysis iteration by adding to the corpus of exclusion and through the manual retrieval of collocations. The program consequently posesses a limited amount of flexibility in this area. In summary, NeoloSearch attempts two goals: to provide a statistical understanding of neologisms and their relationship to productivity and to provide an automated neologism retrieval and analysis system.