Lifelong Machine Learning for Topic Modeling and Beyond
Zhiyuan Chen · 2015
Machine learning has been popularly used in numerous natural language processing tasks.However, most machine learning models are built using a single dataset.This is often referred to as one-shot learning.Although this one-shot learning paradigm is very useful, it will never make an NLP system understand the natural language because it does not accumulate knowledge learned in the past and make use of the knowledge in future learning and problem solving.In this thesis proposal, I first present a survey of lifelong machine learning (LML).I then narrow down to one specific NLP task, i.e., topic modeling.I propose several approaches to apply lifelong learning idea in topic modeling.Such capability is essential to make an NLP system versatile and holistic.