The past decade has seen an explosion in the amount of digital information stored in
electronic health records (EHRs). While primarily designed for archiving patient information …

We study relation extraction for knowledge base (KB) enrichment. Specifically, we aim to
extract entities and their relationships from sentences in the form of triples and map the …

There have been significant advancements in catalysis today, especially with the aid of
artificial intelligence (AI). Understanding the relationship between the descriptors and …

Machine learning (ML) has widespread applications and has revolutionized many
industries, but suffers from several challenges. First, sufficient high-quality training data is …

Providing machines with comprehensive knowledge of the world's entities and their
relationships has been a long-standing vision and challenge for AI. Over the last 15 years …

Existing methods in relation extraction have leveraged the lexical features in the word
sequence and the syntactic features in the parse tree. Though effective, the lexical features …

Manual annotation of labeled data for relation extraction is time-consuming and labor-
intensive. Semi-supervised methods can offer helping hands for this problem and have …

The performance of relation extraction (RE) is hindered by the lack of sufficient labeled data.
Semisupervised methods can offer to help hands with this problem by augmenting high …

Abstract Knowledge base construction has recently been extended to fictional domains like
multivolume novels and TV/movie series, aiming to support explorative queries for fans and …

Manual annotation of the labeled data for relation extraction is time-consuming and labor-
intensive. Semi-supervised methods can offer helping hands for this problem and have …