  Mined Semantic Analysis (MSA) is a novel concept space model which employs
unsupervised learning to generate semantic representations of text. MSA
represents textual structures (terms, phrases, documents) as a Bag of Concepts
(BoC) where concepts are derived from concept rich encyclopedic corpora.
Traditional concept space models exploit only target corpus content to
construct the concept space. MSA, alternatively, uncovers implicit relations
between concepts by mining for their associations (e.g., mining Wikipedia's
"See also" link graph). We evaluate MSA's performance on benchmark datasets for
measuring semantic relatedness of words and sentences. Empirical results show
competitive performance of MSA compared to prior state-of-the-art methods.
Additionally, we introduce the first analytical study to examine statistical
significance of results reported by different semantic relatedness methods. Our
study shows that, the nuances of results across top performing methods could be
statistically insignificant. The study positions MSA as one of state-of-the-art
methods for measuring semantic relatedness, besides the inherent
interpretability and simplicity of the generated semantic representation.
