  Language identification is a critical component of language processing
pipelines (Jauhiainen et al.,2019) and is not a solved problem in real-world
settings. We present a lightweight and effective language identifier that is
robust to changes of domain and to the absence of copious training data.
  The key idea for classification is that the reciprocal of the rank in a
frequency table makes an effective additive feature score, hence the term
Reciprocal Rank Classifier (RRC). The key finding for language classification
is that ranked lists of words and frequencies of characters form a sufficient
and robust representation of the regularities of key languages and their
orthographies.
  We test this on two 22-language data sets and demonstrate zero-effort domain
adaptation from a Wikipedia training set to a Twitter test set. When trained on
Wikipedia but applied to Twitter the macro-averaged F1-score of a
conventionally trained SVM classifier drops from 90.9% to 77.7%. By contrast,
the macro F1-score of RRC drops only from 93.1% to 90.6%. These classifiers are
compared with those from fastText and langid. The RRC performs better than
these established systems in most experiments, especially on short Wikipedia
texts and Twitter.
  The RRC classifier can be improved for particular domains and conversational
situations by adding words to the ranked lists. Using new terms learned from
such conversations, we demonstrate a further 7.9% increase in accuracy of
sample message classification, and 1.7% increase for conversation
classification. Surprisingly, this made results on Twitter data slightly worse.
  The RRC classifier is available as an open source Python package
(https://github.com/LivePersonInc/lplangid).
