Open AccessEnhancing the performance of cancer text classification model based on cancer hallmarks
Author(s) -
Noha Ali,
Ahmed H. AbuEl-Atta,
Hala H. Zayed
Publication year - 2021
Publication title -
iaes international journal of artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2252-8938
pISSN - 2089-4872
DOI - 10.11591/ijai.v10.i2.pp316-323
Subject(s) - word embedding , phrase , computer science , embedding , word (group theory) , span (engineering) , artificial intelligence , convolutional neural network , natural language processing , deep learning , recurrent neural network , artificial neural network , representation (politics) , speech recognition , pattern recognition (psychology) , mathematics , civil engineering , politics , law , political science , engineering , geometry
Deep learning (DL) algorithms achieved state-of-the-art performance in computer vision, speech recognition, and natural language processing (NLP). In this paper, we enhance the convolutional neural network (CNN) algorithm to classify cancer articles according to cancer hallmarks. The model implements a recent word embedding technique in the embedding layer. This technique uses the concept of distributed phrase representation and multi-word phrases embedding. The proposed model enhances the performance of the existing model used for biomedical text classification. The result of the proposed model overcomes the previous model by achieving an F-score equal to 83.87% using an unsupervised technique that trained on PubMed abstracts called PMC vectors (PMCVec) embedding. Also, we made another experiment on the same dataset using the recurrent neural network (RNN) algorithm with two different word embeddings Google news and PMCVec which achieving F-score equal to 74.9% and 76.26%, respectively.