Relation extraction is a subtask of information extraction where semantic relationships are
extracted from natural language text and then classified. In essence, it allows us to acquire …

Development of machine learning (ML) workflows is a tedious process of iterative
experimentation: developers repeatedly make changes to workflows until the desired …

Data collection is a major bottleneck in machine learning and an active research topic in
multiple communities. There are largely two reasons data collection has recently become a …

Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …

Objective: Patient notes in electronic health records (EHRs) may contain critical information
for medical investigations. However, the vast majority of medical investigators can only …

Machine learning has become an essential tool for gleaning knowledge from data and
tackling a diverse set of computationally hard tasks. However, the accuracy of a machine …

This article provides a comprehensive overview of the broad area of semantic search on text
and knowledge bases. In a nutshell, semantic search is “search with meaning”. This …

With the rapid growth of information technology and sports, analyzing sports information has
become an increasingly challenging issue. Sports big data come from the Internet and show …

Machine learning workflow development is a process of trial-and-error: developers iterate on
workflows by testing out small modifications until the desired accuracy is achieved …

Embedding models have been recognized as an effective learning paradigm for high-
dimensional data. However, a major embedding model training obstacle is that updating …