  In this paper we present a fundamental lexical semantics of Sinhala language
and a Hidden Markov Model (HMM) based Part of Speech (POS) Tagger for Sinhala
language. In any Natural Language processing task, Part of Speech is a very
vital topic, which involves analysing of the construction, behaviour and the
dynamics of the language, which the knowledge could utilized in computational
linguistics analysis and automation applications. Though Sinhala is a
morphologically rich and agglutinative language, in which words are inflected
with various grammatical features, tagging is very essential for further
analysis of the language. Our research is based on statistical based approach,
in which the tagging process is done by computing the tag sequence probability
and the word-likelihood probability from the given corpus, where the linguistic
knowledge is automatically extracted from the annotated corpus. The current
tagger could reach more than 90% of accuracy for known words.
