Lifelong Machine Learning for Topic Modeling Based on Hellinger Distance
Mohammad Kamel Daradkeh, Wathiq Mansoor, Shadi Atalla, Yassine Himeur, Oussama Kerdjidj · 2023
This paper proposes an improved version of the Lifelong Topic Model (LTM) called the HC-LTM. The traditional LTM is known to be biased in the domain selection process and does not fully consider the contextual information of target words when determining similarity. The HC-LTM addresses these issues by combining Word2vec cosine similarity and Hellinger distance between topics to identify similar words and topics, leading to better selection and more effective knowledge acquisition during iterative learning. Additionally, the problem of repetitive calculation of cosine distance is resolved by pre-loading the similarity matrix of word vectors and using Hellinger distance to calculate topic similarity accelerates the convergence of the model. The experimental results on the Amazon product review dataset demonstrate the effectiveness of the HC-LTM model, with a 49% improvement in topic consistency and a 44.57% reduction in time compared to the LTM model.