  Various applications in computational linguistics and artificial intelligence
rely on high-performing word sense disambiguation techniques to solve
challenging tasks such as information retrieval, machine translation, question
answering, and document clustering. While text comprehension is intuitive for
humans, machines face tremendous challenges in processing and interpreting a
human's natural language. This paper presents a novel knowledge-based word
sense disambiguation algorithm, namely Sequential Contextual Similarity Matrix
Multiplication (SCSMM). The SCSMM algorithm combines semantic similarity,
heuristic knowledge, and document context to respectively exploit the merits of
local context between consecutive terms, human knowledge about terms, and a
document's main topic in disambiguating terms. Unlike other algorithms, the
SCSMM algorithm guarantees the capture of the maximum sentence context while
maintaining the terms' order within the sentence. The proposed algorithm
outperformed all other algorithms when disambiguating nouns on the combined
gold standard datasets, while demonstrating comparable results to current
state-of-the-art word sense disambiguation systems when dealing with each
dataset separately. Furthermore, the paper discusses the impact of granularity
level, ambiguity rate, sentence size, and part of speech distribution on the
performance of the proposed algorithm.
