Because of large amounts of unstructured data generated on the Internet, entity relation
extraction is believed to have high commercial value. Entity relation extraction is a case of …

We introduce a multi-task setup of identifying and classifying entities, relations, and
coreference clusters in scientific articles. We create SciERC, a dataset that includes …

Pre-trained word embeddings learned from unlabeled text have become a standard
component of neural network architectures for NLP tasks. However, in most cases, the …

We describe a deployed scalable system for organizing published scientific literature into a
heterogeneous graph to facilitate algorithmic manipulation and discovery. The resulting …

We describe the SemEval task of extracting keyphrases and relations between them from
scientific documents, which is crucial for understanding which publications describe which …

In scientific research, the method is an indispensable means to solve scientific problems and
a critical research object. With the advancement of sciences, many scientific methods are …

This work presents a two-stage deep learning system for Named Entity Recognition (NER)
and Relation Extraction (RE) from medical texts. These tasks are a crucial step to many …

This paper studies the end-to-end construction of an NLP Knowledge Graph (KG) from
scientific papers. We focus on extracting four types of relations: evaluatedOn between tasks …

We investigate the incorporation of character-based word representations into a standard
CNN-based relation extraction model. We experiment with two common neural …

We introduce the STEM (Science, Technology, Engineering, and Medicine) Dataset for
Scientific Entity Extraction, Classification, and Resolution, version 1.0 (STEM-ECR v1. 0) …