Labeling training data has become one of the major roadblocks to using machine learning.
Among various weak supervision paradigms, programmatic weak supervision (PWS) has …

Weak Supervision (WS) techniques allow users to efficiently create large training datasets
by programmatically labeling data with heuristic sources of supervision. While the success of …

An important task for enabling the efficient exploration of available data in a data lake is to
annotate semantic type information to the available data sources. In order to reduce the …

Effective supervised training of modern machine learning models often requires large
labelled training datasets, which could be prohibitively costly to acquire for many practical …

In this paper, we introduce STEER to adapt learned semantic type extraction approaches to
a new, unseen data lake. STEER provides a data programming framework for semantic …

This study introduces an innovative automatic labeling framework to address the challenges
of lexical normalization in social media texts for low-resource languages like Vietnamese …

A key challenge in development of reliable and robust medical imaging machine learning
solution is the lack of annotated data. This problem becomes particularly significant when …

Supervised machine learning is becoming increasingly popular-and so is the need for
annotated training data. Such data often needs to be manually labeled by human workers …

Social Networking Sites (SNS) are the most important way of communication nowadays.
They have changed how we interact with our friends and family, and even how companies …

Social Networking Sites (SNS) offer a full set of possibilities to perform opinion studies such
as polling or market analysis. Normally, artificial intelligence techniques are applied, and …