Mined Semantic Analysis: A New Concept Space Model for Semantic Representation of Textual Data
This addresses the problem of semantic representation for natural language processing tasks, but it is incremental as it builds on existing concept space models with external data mining.
The paper tackles semantic representation of text by introducing Mined Semantic Analysis (MSA), a concept space model that uses unsupervised learning and external encyclopedic corpora to generate Bag of Concepts representations, achieving competitive performance on benchmark datasets for semantic relatedness.
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.