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.

An ecology-based index for text embedding and classification / Martino, Alessio; De Santis, Enrico; Rizzi, Antonello. - 2020 International Joint Conference on Neural Networks (IJCNN), (2020), pp. 1-8. (IJCNN 2020 - 2020 International Joint Conference on Neural Networks, Online Event due to COVID-19 (formerly Glasgow, UK), 19-24 July 2020). [10.1109/IJCNN48605.2020.9207299].

An ecology-based index for text embedding and classification

Alessio Martino
;
2020

Abstract

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.
2020
978-1-7281-6926-2
embedding spaces, explainable artificial intelligence, granular computing, natural language processing, supervised learning, support vector machine, text classification
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11385/214505
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