<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This paper presents a meta-analysis of experiments performed with language combinatorics (LC), a novel language model generation and feature extraction method based on combinatorial manipulations of sentence elements (e.g., words). Along recent years LC has been applied to a number of text classification tasks, such as affect analysis, cyberbullying detection or future reference extraction. We summarize two of the most extensive experiments and discuss general implications for future implementations of combinatorial language model.</p></abstract>

This paper presents a meta-analysis of experiments performed with language combinatorics (LC), a novel language model generation and feature extraction method based on combinatorial manipulations of sentence elements (e.g., words). Along recent years LC has been applied to a number of text classification tasks, such as affect analysis, cyberbullying detection or future reference extraction. We summarize two of the most extensive experiments and discuss general implications for future implementations of combinatorial language model.

Experiments with language combinatorics in text classification: lessons learned and future implications

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This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This paper presents a meta-analysis of experiments performed with language combinatorics (LC), a novel language model generation and feature extraction method...

Experiments with language combinatorics in text classification: lessons learned and future implications

Publié en ligne: 29 mai 2020

Pages: 183 - 197

DOI: https://doi.org/10.4467/2353737XCT.17.200.7429

Mots clés
language combinatorics, natural language processing, text classification

© 2017 Michal Ptaszynski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Experiments with language combinatorics in text classification: lessons learned and future implications

Publié en ligne: 29 mai 2020

Pages: 183 - 197

DOI: https://doi.org/10.4467/2353737XCT.17.200.7429

Mots cléslanguage combinatorics, natural language processing, text classification

© 2017 Michal Ptaszynski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
language combinatorics, natural language processing, text classification