Most icon-based augmentative and alternative communication (AAC) devices require users to formulate messages in syntactic order in order to produce syntactic utterances. Reliance on syntactic ordering, however, may not be appropriate for individuals with limited or emerging literacy skills. Some of these users may benefit from unordered message formulation accompanied by automatic message expan...

Classified s-grams have been successfully used in cross-language information retrieval (CLIR) as an approximate string matching technique for translating out-of-vocabulary (OOV) words. For example, s-grams have consistently outperformed other approximate string matching techniques, like edit distance or n-grams. The Jaccard coefficient has traditionally been used as an s-gram based string proxi...

CNG Method for Authorship Attribution. The Common N-Grams (CNG) classification method for authorship attribution (AATT) was described in [2]. The method is based on extracting the most frequent byte n-grams of size n from the training data. The n-grams are sorted by their normalized frequency, and the first L most-frequent n-grams define an author profile. Given a test document, the test profil...

We use web-scale N-grams in a base NP parser that correctly analyzes 95.4% of the base NPs in natural text. Web-scale data improves performance. That is, there is no data like more data. Performance scales log-linearly with the number of parameters in the model (the number of unique N-grams). The web-scale N-grams are particularly helpful in harder cases, such as NPs that contain conjunctions.