Abstract
Abstract Traditional machine learning techniques follow a single shot learning approach. It includes all supervised, semi-supervised, transfer learning, hybrid and unsupervised techniques having a single target domain known prior to analysis. Learning from one task is not carried to the next task, therefore, they cannot scale up to big data having many unknown domains. Lifelong learning models are tailored for big data having a knowledge module that is maintained automatically. The knowledge-base grows with experience where knowledge from previous tasks helps in current task. This paper surveys topic models leading the discussion to knowledge-based topic models and lifelong learning models. The issues and challenges in learning knowledge, its abstraction, retention and transfer are elaborated. The state-of-the art models store word pairs as knowledge having positive or negative co-relations called must-links and cannot-links. The need for innovative ideas from other research fields is stressed to learn more varieties of knowledge to improve accuracy and reveal more semantic structures from within the data.
Highlights
Probabilistic topic models perform statistical evaluations on words co-occurrence to extract popular words and group them in topics
Lifelong machine learning (LML) models are recently used for NLP tasks
Complex networks can be used as a learning module for LML models
Summary
Probabilistic topic models perform statistical evaluations on words co-occurrence to extract popular words and group them in topics. Knowledge-base topic models are provided with domain specific knowledge rules instead of seed aspects, following a semi-supervised approach. Complex networks helps to compartmentalize different NLP tasks and provide visualization to help analyze the problem It can be used as a knowledge-base in support with a machine learning technique. The semi-supervised topic models required user guidance as initial seeds in order to extract more aspects They provide high accuracy with guided inference to produce aspect distribution confirming to user’s needs. The issue was addressed through Automatic knowledge-based model that learns and apply knowledge without any external support It can process many unknown domains in big data and can extract and process knowledge from within, by exploiting the large volume of data.
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