Abstract
Lifelong machine learning is a novel machine learning paradigm which continually learns tasks and accumulates knowledge for reuse. The knowledge extracting and reusing abilities enable lifelong machine learning to understand the knowledge for solving a task and obtain the ability to solve the related problems. In sentiment classification, traditional approaches like Naive Bayes focus on the probability for each word with positive or negative sentiment. However, the lifelong machine learning in this paper will investigate this problem in a different angle and attempt to discover which words determine the sentiment of a review. We will pay all attention to obtain knowledge during learning for future learning rather than just solve a current task.
Abstract (translated)
终身机器学习是一种新的机器学习范式，它不断地学习任务，积累知识以备重用。知识提取和重用能力使终身机器学习者能够理解解决任务的知识，并获得解决相关问题的能力。在情感分类中，传统的方法如朴素贝叶斯（naive bayes）关注的是每一个词的概率，包括正面或负面情感。然而，本文中的终身机器学习将从不同的角度来研究这个问题，并试图发现哪些词语决定了一篇评论的情感。我们将在学习过程中全神贯注地获取知识，为将来的学习服务，而不仅仅是解决当前的任务。
URL
https://arxiv.org/abs/1905.01988
