  Most languages, especially in Africa, have fewer or no established
part-of-speech (POS) tagged corpus. However, POS tagged corpus is essential for
natural language processing (NLP) to support advanced researches such as
machine translation, speech recognition, etc. Even in cases where there is no
POS tagged corpus, there are some languages for which parallel texts are
available online. The task of POS tagging a new language corpus with a new
tagset usually face a bootstrapping problem at the initial stages of the
annotation process. The unavailability of automatic taggers to help the human
annotator makes the annotation process to appear infeasible to quickly produce
adequate amounts of POS tagged corpus for advanced NLP research and training
the taggers. In this paper, we demonstrate the efficacy of a POS annotation
method that employed the services of two automatic approaches to assist POS
tagged corpus creation for a novel language in NLP. The two approaches are
cross-lingual and monolingual POS tags projection. We used cross-lingual to
automatically create an initial 'errorful' tagged corpus for a target language
via word-alignment. The resources for creating this are derived from a source
language rich in NLP resources. A monolingual method is applied to clean the
induce noise via an alignment process and to transform the source language tags
to the target language tags. We used English and Igbo as our case study. This
is possible because there are parallel texts that exist between English and
Igbo, and the source language English has available NLP resources. The results
of the experiment show a steady improvement in accuracy and rate of tags
transformation with score ranges of 6.13% to 83.79% and 8.67% to 98.37%
respectively. The rate of tags transformation evaluates the rate at which
source language tags are translated to target language tags.
