  This paper explores two separate questions: Can we perform natural language
processing tasks without a lexicon?; and, Should we? Existing natural language
processing techniques are either based on words as units or use units such as
grams only for basic classification tasks. How close can a machine come to
reasoning about the meanings of words and phrases in a corpus without using any
lexicon, based only on grams?
  Our own motivation for posing this question is based on our efforts to find
popular trends in words and phrases from online Chinese social media. This form
of written Chinese uses so many neologisms, creative character placements, and
combinations of writing systems that it has been dubbed the "Martian Language."
Readers must often use visual queues, audible queues from reading out loud, and
their knowledge and understanding of current events to understand a post. For
analysis of popular trends, the specific problem is that it is difficult to
build a lexicon when the invention of new ways to refer to a word or concept is
easy and common. For natural language processing in general, we argue in this
paper that new uses of language in social media will challenge machines'
abilities to operate with words as the basic unit of understanding, not only in
Chinese but potentially in other languages.
