Lifelong Machine Learning for Topic Modeling and Beyond
نویسنده
چکیده
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.
منابع مشابه
Machine Learning and Citizen Science: Opportunities and Challenges of Human-Computer Interaction
Background and Aim: In processing large data, scientists have to perform the tedious task of analyzing hefty bulk of data. Machine learning techniques are a potential solution to this problem. In citizen science, human and artificial intelligence may be unified to facilitate this effort. Considering the ambiguities in machine performance and management of user-generated data, this paper aims to...
متن کاملTopic Modeling using Topics from Many Domains, Lifelong Learning and Big Data
Topic modeling has been commonly used to discover topics from document collections. However, unsupervised models can generate many incoherent topics. To address this problem, several knowledge-based topic models have been proposed to incorporate prior domain knowledge from the user. This work advances this research much further and shows that without any user input, we can mine the prior knowle...
متن کاملMachine learning algorithms in air quality modeling
Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affec...
متن کاملRelationship of Academic Vitality, Academic Self-efficacy, and Metacognitive Skills with Lifelong Learning Concerning Mediating role of Study Approaches
Background: Nowadays, learning is one of the most important factors in the lives of human beings. Lifelong learning and its effective variables are the topics of discussion in the contemporary era. In this regard, the present study aimed to investigate the relationship of academic vitality, academic self-efficacy, and metacognitive skills with lifelong learning concerning the mediating role of ...
متن کاملLifelong Machine Learning Systems: Beyond Learning Algorithms
Lifelong Machine Learning, or LML, considers systems that can learn many tasks from one or more domains over its lifetime. The goal is to sequentially retain learned knowledge and to selectively transfer that knowledge when learning a new task so as to develop more accurate hypotheses or policies. Following a review of prior work on LML, we propose that it is now appropriate for the AI communit...
متن کامل