Using Recurrent Neural Networks for Part-of-Speech Tagging and Subject and Predicate Classification in a Sentence

Authors

David Muñoz-Valero, Luis Rodriguez-Benitez, Luis Jimenez-Linares, Juan Moreno-Garcia^*,

Department of Information Technologies and Systems, University of Castilla-La Mancha, Avenida Carlos III, Toledo, Spain

^*Corresponding author. Email: juan.moreno@uclm.es

DOI

10.2991/ijcis.d.200527.005How to use a DOI?

Keywords

Natural language processing; Part-of-speech (POS) tagging; Recurrent neural networks; Syntactic analysis

Abstract

In natural language processing the use of deep learning techniques is very common. In this paper, a technique to identify the subject and predicate in a sentence is introduced. To achieve this, the proposed technique completes POS tagging identifying in a later stage the subject and the predicate in a sentence. Two different deep neural networks are used to complete this process. A first one to establish a correspondence between individual words and part-of-speech (POS) tags and a second one that, taking as input these tags, identifies relevant elements of the sentence such like the subject and the predicate. To validate the architecture of our proposal a set of tests over public datasets have been designed. In these experiments, this model achieves high rates of accuracy in POS tagging and in subject and predicate classification. Finally, a comparison of the results obtained for each individual network with similar tools such as NLTK, pyStatParser and spaCy is made.

Copyright

© 2020 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - David Muñoz-Valero
AU  - Luis Rodriguez-Benitez
AU  - Luis Jimenez-Linares
AU  - Juan Moreno-Garcia
PY  - 2020
DA  - 2020/06/17
TI  - Using Recurrent Neural Networks for Part-of-Speech Tagging and Subject and Predicate Classification in a Sentence
JO  - International Journal of Computational Intelligence Systems
SP  - 706
EP  - 716
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200527.005
DO  - 10.2991/ijcis.d.200527.005
ID  - Muñoz-Valero2020
ER  -