Ricerc@Sapienza

Accurate semantic representation models are essential in text mining applications. For a successful application of the text mining process, the text representation adopted must keep the interesting patterns to be discovered. Although competitive results for automatic text classification may be achieved with traditional bag of words, such representation model cannot provide satisfactory classification performances on hard settings where richer text representations are required.

Natural language processing and text mining applications have gained a growing attention and diffusion in the computer science and machine learning communities. In this work, a new embedding scheme is proposed for solving text classification problems. The embedding scheme relies on a statistical assessment of relevant words within a corpus using a compound index originally proposed in ecology: this allows to spot relevant parts of the overall text (e.g., words) on the top of which the embedding is performed following a Granular Computing approach.