With the advent of Web 2.0, there exist many online platforms that result in massive textual-
data production. With ever-increasing textual data at hand, it is of immense importance to …

Relation extraction (RE) involves identifying the relations between entities from underlying
content. RE serves as the foundation for many natural language processing (NLP) and …

As the amount of generated information grows, reading and summarizing texts of large
collections turns into a challenging task. Many documents do not come with descriptive …

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 …

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

Extracting structured clinical information from free-text radiology reports can enable the use
of radiology report information for a variety of critical healthcare applications. In our work, we …

Pretrained language models (PTLMs) are typically learned over a large, static corpus and
further fine-tuned for various downstream tasks. However, when deployed in the real world …

Due to the considerable growth of the volume of text documents on the Internet and in digital
libraries, manual analysis of these documents is no longer feasible. Having efficient …

We propose an unsupervised keyphrase extraction model that encodes topical information
within a multipartite graph structure. Our model represents keyphrase candidates and topics …

Keyphrase extraction is a textual information processing task concerned with the automatic
extraction of representative and characteristic phrases from a document that express all the …