Online Knowledge-Based Model for Big Data Topic Extraction

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 ...

Asymmetric author-topic model for knowledge discovering of big data in toxicogenomics

The advancement of high-throughput screening technologies facilitates the generation of massive amount of biological data, a big data phenomena in biomedical science. Yet, researchers still heavily rely on keyword search and/or literature review to navigate the databases and analyses are often done in rather small-scale. As a result, the rich information of a database has not been fully utilize...

Model-Parallel Inference for Big Topic Models

In real world industrial applications of topic modeling, the ability to capture gigantic conceptual space by learning an ultra-high dimensional topical representation, i.e., the so-called “big model”, is becoming the next desideratum after enthusiasms on ”big data”, especially for fine-grained downstream tasks such as online advertising, where good performances are usually achieved by regressio...

Lifelong aspect extraction from big data: knowledge engineering

Background Probabilistic topic models perform statistical evaluations on words co-occurrence to extract popular words and group them in topics. A topic can be considered as a concept represented through its top words. In aspect based sentiment analysis (ABSA), topics are used to represent product aspects or sentiment category. Due to the amount of content produced online, there is rich informat...

Topic Extraction based on Prior Knowledge obtained from Target Documents