  The task of automatically identifying a language used in a given text is
called Language Identification (LI). India is a multilingual country and many
Indians especially youths are comfortable with Hindi and English, in addition
to their local languages. Hence, they often use more than one language to post
their comments on social media. Texts containing more than one language are
called "code-mixed texts" and are a good source of input for LI. Languages in
these texts may be mixed at sentence level, word level or even at sub-word
level. LI at word level is a sequence labeling problem where each and every
word in a sentence is tagged with one of the languages in the predefined set of
languages. In order to address word level LI in code-mixed Kannada-English
(Kn-En) texts, this work presents i) the construction of code-mixed Kn-En
dataset called CoLI-Kenglish dataset, ii) code-mixed Kn-En embedding and iii)
learning models using Machine Learning (ML), Deep Learning (DL) and Transfer
Learning (TL) approaches. Code-mixed Kn-En texts are extracted from Kannada
YouTube video comments to construct CoLI-Kenglish dataset and code-mixed Kn-En
embedding. The words in CoLI-Kenglish dataset are grouped into six major
categories, namely, "Kannada", "English", "Mixed-language", "Name", "Location"
and "Other". The learning models, namely, CoLI-vectors and CoLI-ngrams based on
ML, CoLI-BiLSTM based on DL and CoLI-ULMFiT based on TL approaches are built
and evaluated using CoLI-Kenglish dataset. The performances of the learning
models illustrated, the superiority of CoLI-ngrams model, compared to other
models with a macro average F1-score of 0.64. However, the results of all the
learning models were quite competitive with each other.
