<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Dependency parsing is a complex process in natural language text processing, text to semantic transformation. The efficiency improvement of dependency parsing is a current and an active research area in the NLP community. The paper presents four transition-based dependency parser models with implementation using DL4J classifiers. The efficiency of the proposed models were tested with Hungarian language corpora. The parsing model uses a data representation form based on lightweight embedding and a novel morphological-description-vector format is proposed for the input layer. Based on the test experiments on parsing Hungarian text documents, the proposed list-based transitions parsers outperform the widespread stack-based variants.</p>
</abstract>

Dependency parsing is a complex process in natural language text processing, text to semantic transformation. The efficiency improvement of dependency parsing is a current and an active research area in the NLP community. The paper presents four transition-based dependency parser models with implementation using DL4J classifiers. The efficiency of the proposed models were tested with Hungarian language corpora. The parsing model uses a data representation form based on lightweight embedding and a novel morphological-description-vector format is proposed for the input layer. Based on the test experiments on parsing Hungarian text documents, the proposed list-based transitions parsers outperform the widespread stack-based variants.

Efficiency Analysis of Deeplearning4J Neural Network Classifiers in Development of Transition Based Dependency Parsers

Acta Marisiensis. Seria Technologica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Dependency parsing is a complex process in natural language text processing, text to semantic transformation. The efficiency improvement of dependency parsing...

Efficiency Analysis of Deeplearning4J Neural Network Classifiers in Development of Transition Based Dependency Parsers

Publié en ligne: 29 mai 2021

Pages: 33 - 39

DOI: https://doi.org/10.2478/amset-2021-0006

Mots clés
NLP, dependency parser, word embedding, lightweight word embedding

© 2021 László Csépányi-Fürjes et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Efficiency Analysis of Deeplearning4J Neural Network Classifiers in Development of Transition Based Dependency Parsers

Publié en ligne: 29 mai 2021

Pages: 33 - 39

DOI: https://doi.org/10.2478/amset-2021-0006

Mots clésNLP, dependency parser, word embedding, lightweight word embedding

© 2021 László Csépányi-Fürjes et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
NLP, dependency parser, word embedding, lightweight word embedding