  The improvement in Information Technology has encouraged the use of Igbo in
the creation of text such as resources and news articles online. Text
similarity is of great importance in any text-based applications. This paper
presents a comparative analysis of n-gram text representation on Igbo text
document similarity. It adopted Euclidean similarity measure to determine the
similarities between Igbo text documents represented with two word-based n-gram
text representation (unigram and bigram) models. The evaluation of the
similarity measure is based on the adopted text representation models. The
model is designed with Object-Oriented Methodology and implemented with Python
programming language with tools from Natural Language Toolkits (NLTK). The
result shows that unigram represented text has highest distance values whereas
bigram has the lowest corresponding distance values. The lower the distance
value, the more similar the two documents and better the quality of the model
when used for a task that requires similarity measure. The similarity of two
documents increases as the distance value moves down to zero (0). Ideally, the
result analyzed revealed that Igbo text document similarity measured on bigram
represented text gives accurate similarity result. This will give better,
effective and accurate result when used for tasks such as text classification,
clustering and ranking on Igbo text.
