  Fuzzy string matching and language classification are important tools in
Natural Language Processing pipelines, this paper provides advances in both
areas. We propose a fast novel approach to string tokenisation for fuzzy
language matching and experimentally demonstrate an 83.6% decrease in
processing time with an estimated improvement in recall of 3.1% at the cost of
a 2.6% decrease in precision. This approach is able to work even where keywords
are subdivided into multiple words, without needing to scan
character-to-character. So far there has been little work considering using
metadata to enhance language classification algorithms. We provide
observational data and find the Accept-Language header is 14% more likely to
match the classification than the IP Address.
