An ecology-based index for text embedding and classification

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. The employment of statistically meaningful words not only eases the computational burden and the embedding space dimensionality, but also returns a more interpretable model. Our approach is tested on both synthetic datasets and benchmark datasets against well-known embedding techniques, with remarkable results both in terms of performances and computational complexity.