Open AccessPerbandingan Pre-trained Word Embedding dan Embedding Layer untuk Named-Entity Recognition Bahasa Indonesia
Author(s) -
Meredita Susanty,
Sahrul Sukardi
Publication year - 2021
Publication title -
petir/petir (jakarta. online)
Language(s) - English
Resource type - Journals
eISSN - 2655-5018
pISSN - 1978-9262
DOI - 10.33322/petir.v14i2.1164
Subject(s) - embedding , computer science , artificial intelligence , word2vec , word embedding , word (group theory) , layer (electronics) , unsupervised learning , pattern recognition (psychology) , process (computing) , natural language processing , supervised learning , machine learning , artificial neural network , mathematics , chemistry , geometry , organic chemistry , operating system
Named-Entity Recognition (NER) is used to extract information from text by identifying entities such as the name of the person, organization, location, time, and other entities. Recently, machine learning approaches, particularly deep-learning, are widely used to recognize patterns of entities in sentences. Embedding, a process to convert text data into a number or vector of numbers, translates high dimensional vectors into relatively low-dimensional space. Embeddings make it easier to do machine learning on large inputs like sparse vectors representing words. The embedding process can be performed using the supervised learning method, which requires a large number of labeled data sets or an unsupervised learning approach. This study compares the two embedding methods; trainable embedding layer (supervised learning) and pre-trained word embedding (unsupervised learning). The trainable embedding layer uses the embedding layer provided by the Keras library while pre-trained word embedding uses word2vec, GloVe, and fastText to build NER using the BiLSTM architecture. The results show that GloVe had better performance than other embedding techniques with a micro average f1 score of 76.48.