Newly coined words pose problems for natural language processing systems because they are not in a system's lexicon, and therefore no lexical information is available for such words. A common way to form new words is lexical blending, as in cosmeceutical, a blend of cosmetic and pharmaceutical . We propose a statistical model for inferring a blend's source words drawing on observed linguistic properties of blends; these properties are largely based on the recognizability of the source words in a blend. We annotate a set of 1,186 recently coined expressions which includes 515 blends, and evaluate our methods on a 324-item subset. In this first study of novel blends we achieve an accuracy of 40% on the task of inferring a blend's source words, which corresponds to a reduction in error rate of 39% over an informed baseline. We also give preliminary results showing that our features for source word identification can be used to distinguish blends from other kinds of novel words.

Idiomatic expressions are plentiful in everyday language, yet they remain mysterious, as it is not clear exactly how people learn and understand them. They are of special interest to linguists, psycholinguists, and lexicographers, mainly because of their syntactic and semantic idiosyncrasies as well as their unclear lexical status. Despite a great deal of research on the properties of idioms in the linguistics literature, there is not much agreement on which properties are characteristic of these expressions. Because of their peculiarities, idiomatic expressions have mostly been overlooked by researchers in computational linguistics. In this article, we look into the usefulness of some of the identified linguistic properties of idioms for their automatic recognition. Specifically, we develop statistical measures that each model a specific property of idiomatic expressions by looking at their actual usage patterns in text. We use these statistical measures in a type-based classification task where we automatically separate idiomatic expressions (expressions with a possible idiomatic interpretation) from similar-on-the-surface literal phrases (for which no idiomatic interpretation is possible). In addition, we use some of the measures in a token identification task where we distinguish idiomatic and literal usages of potentially idiomatic expressions in context.