Measuring Semantic Relatedness using Mined Semantic Analysis
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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 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 data sets for measuring lexical semantic relatedness. 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.
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