Indonesian Language Term Extraction using Multi-Task Neural Network

Authors

Joan Santoso, Institut Sains dan Teknologi Terpadu SurabayaFollow
Esther Irawati Setiawan, Institut Sains dan Teknologi Terpadu SurabayaFollow
Fransiskus Xaverius Ferdinandus, Institut Sains dan Teknologi Terpadu SurabayaFollow
Gunawan Gunawan, Institut Sains dan Teknologi Terpadu SurabayaFollow
Leonel Hernandez Collantes, Institucion Universitaria de Barranquilla IUBFollow

Abstract

The rapidly expanding size of data makes it difficult to extricate information and store it as computerized knowledge. Relation extraction and term extraction play a crucial role in resolving this issue. Automatically finding a concealed relationship between terms that appear in the text can help people build computer-based knowledge more quickly. Term extraction is required as one of the components because identifying terms that play a significant role in the text is the essential step before determining their relationship. We propose an end-to-end system capable of extracting terms from text to address this Indonesian language issue. Our method combines two multilayer perceptron neural networks to perform Part-of-Speech (PoS) labeling and Noun Phrase Chunking. Our models were trained as a joint model to solve this problem. Our proposed method, with an f-score of 86.80%, can be considered a state-of-the-art algorithm for performing term extraction in the Indonesian Language using noun phrase chunking.

DOI

https://doi.org/10.17977/um018v5i22022p160-167

Recommended Citation

Santoso, Joan; Setiawan, Esther Irawati; Ferdinandus, Fransiskus Xaverius; Gunawan, Gunawan; and Collantes, Leonel Hernandez (2022) "Indonesian Language Term Extraction using Multi-Task Neural Network," Knowledge Engineering and Data Science: Vol. 5: No. 2, Article 7.
DOI: https://doi.org/10.17977/um018v5i22022p160-167
Available at: https://citeus.um.ac.id/keds/vol5/iss2/7