Mobile or SMS spam is a real and growing problem primarily due to the availability of very
cheap bulk pre-pay SMS packages and the fact that SMS engenders higher response rates …

Twitter provides access to large volumes of data in real time, but is notoriously noisy,
hampering its utility for NLP. In this paper, we target out-of-vocabulary words in short text …

Human civilizations have performed the art of writing across continents and over different
time periods. In order to speed up the writing process, the art of shorthand (brachygraphy) …

Abstract Machine learning, including neural network techniques, have been applied to
virtually every domain in natural language processing. One problem that has been …

Social media language contains huge amount and wide variety of nonstandard tokens,
created both intentionally and unintentionally by the users. It is of crucial importance to …

This paper presents a challenge to the community: given a large corpus of written text
aligned to its normalized spoken form, train an RNN to learn the correct normalization …

The proliferation of Web 2.0 technologies and the increasing use of computer-mediated
communication resulted in a new form of written text, termed microtext. This poses new …

Most text message normalization approaches are based on supervised learning and rely on
human labeled training data. In addition, the nonstandard words are often categorized into …

Microtexts, like SMS messages, Twitter posts, and Facebook status updates, are a popular
medium for real-time communication. In this paper, we investigate the writing conventions …

We present a unified unsupervised statistical model for text normalization. The relationship
between standard and non-standard tokens is characterized by a log-linear model …