Muhammad Taimoor Khan

Online Knowledge-Based Model for Big Data Topic Extraction

Lifelong machine learning (LML) models learn with experience maintaining a knowledge-base, without user intervention. Unlike traditional single-domain models they can easily scale up to explore big data. The existing LML models have high data dependency, consume more resources, and do not support streaming data. This paper proposes online LML model (OAMC) to support streaming data with reduced data dependency. With engineering the knowledge-base and introducing new knowledge features the learning pattern of the model is improved for data arriving in pieces. OAMC improves accuracy as topic coherence by 7% for streaming data while reducing the processing cost to half.

Sentiment Analysis for Health Care

Sentiment analysis for health care deals with the diagnosis of health care related problems identified by the patients themselves. It takes the patients opinions into perspective to make policies and modifications that could directly address their problems. Sentiment analysis is used with commercial products to great effect and has outgrown to other application areas. Aspect based analysis of health care, not only recommend the services and treatments but also present their strong features for which they are preferred. Machine learning techniques are used to analyze millions of review documents and conclude them towards an efficient and accurate decision. The supervised techniques have high accuracy but are not extendable to unknown domains while unsupervised techniques have low accuracy. More work is targeted to improve the accuracy of the unsupervised techniques as they are more practical in this time of information flooding. Sentiment Analysis for Health Care

A three-way approach for learning rules in automatic knowledge-based topic models

A three-way approach is proposed for selecting rules in automatic knowledge-based topic models.The selection of rules in the three-way approach is controlled based on a pair of thresholds.Effective thresholds are determined based on a tradeoff between the quantity of rules and the quality of the rules.The game-theoretic rough set (GTRS) model is used to implement the tradeoff.

Multimodal rule transfer into automatic knowledge based topic models

Topic models are used in text analysis to extract domain features and to explore unknown domains. The topic models and its extensions follow traditional machine learning approach as single-shot learning. Automatic knowledge based topic models (AKBTM) filled this gap by learning from each task and carrying it to future tasks as knowledge rules. Most of the research in AKBTM focuses on rule extraction techniques. The transfer of rules is ignored for the most part of it, using single transfer mode. In this research paper, a multimodal rule transfer mechanism is proposed that operate in three modes to transfer the impact of rules into the inference technique. The rules are divided into two bins, based on their quality, as correlation strength. The mode of transferring rules bias is governed by the strength of rule and time phase the inference technique is in. It resulted in 24 points of improvement in topic coherence of the proposed model as compared to state of the art. The efficient and appropriate transfer of rule bias into the model, helped improve performance by 38%.

Self-refining targeted readings recommender system

Huge volume of content is produced on multiple online sources every day. It is not possible for a user to go through these articles and read about topics of interest. Secondly professional articles, blog and forum have many topics discussed in a single discussion. Automatic knowledge-based topic models is a recent approach in Natural Language Processing that extract high quality topics from a large collection of documents. The quality of topics is improved through the models auto-learning mechanism. In this paper, targeted reading content problem is addressed through automatic knowledge-based topic models, as a readings recommender system. The application recommends text documents based on contextual relevance. The learning module helps the model to learn certain rules from each recommendation, in order to recommend more relevant content in future. The contribution of this research work is to augment knowledge based models with contextual recommender systems. An application is developed that recommends targeted readings to the audience while the knowledge-based learning module grows in experience to serve the future users better.

Evolving long-term dependency rules in lifelong learning models

Topic models are extensively used for text analysis to extract prominent concepts as topics in a large collection of documents about a subject domain. They are extended with different approaches to suit various application areas. Automatic knowledge-based topic models are recently introduced to specifically meet the processing needs of large-scale data having many subject domains. The model automatically learns rules across all domains and uses them to improve the results of the current domain by purposefully grouping words into topics to better represent the underlying concept. The existing models apply thresholds on evaluation criteria to learn rules; however, being automatic it may learn wrong, irrelevant or inconsistent rules as well. In this research article the proposed model learns rules and monitors their contributions towards the quality of results. As the model learns new rules, the existing rules undergo refinement and detachment procedures to retain reliable rules only. Experimental results on user reviews from Amazon.com shows improvement in the quality of topics by using fewer rules which advocates the quality of rules and help avoid performance bottleneck at high experience.