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Mined Semantic Analysis (MSA) is a novel concept space model which employsunsupervised learning to generate semantic representations of text. MSArepresents 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 toconstruct the concept space. MSA, alternatively, uncovers implicit relationsbetween concepts by mining for their associations (e.g., mining Wikipedia's"See also" link graph). We evaluate MSA's performance on benchmark datasets formeasuring semantic relatedness of words and sentences. Empirical results showcompetitive performance of MSA compared to prior state-of-the-art methods.Additionally, we introduce the first analytical study to examine statisticalsignificance of results reported by different semantic relatedness methods. Ourstudy shows that, the nuances of results across top performing methods could bestatistically insignificant. The study positions MSA as one of state-of-the-artmethods for measuring semantic relatedness, besides the inherentinterpretability and simplicity of the generated semantic representation.

Mined Semantic Analysis: A New Concept Space Model for Semantic Representation of Textual Data