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

• Published in: International journal of computational intelligence systems Vol. 13; no. 1; pp. 706 - 716
• Main Authors: Muñoz-Valero, David, Rodriguez-Benitez, Luis, Jimenez-Linares, Luis, Moreno-Garcia, Juan
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.01.2020; Springer Nature B.V; Springer
• Subjects: Artificial neural networks; Classification; Indexing; Machine learning; Marking; Model accuracy; Natural language processing; Neural networks; Part-of-speech (POS) tagging; Recurrent neural networks; Research Article; Speech; Syntactic analysis; Tags; Syntactic analysis; Recurrent neural networks; Natural language processing; Part-of-speech (POS) tagging
• ISSN: 1875-6891; 1875-6883; 1875-6883
• DOI: 10.2991/ijcis.d.200527.005

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.