The number of scientific publications in the literature is steadily growing, containing our
knowledge in the biomedical, health, and clinical sciences. Since there is currently no …

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 …

We introduce SpERT, an attention model for span-based joint entity and relation extraction.
Our key contribution is a light-weight reasoning on BERT embeddings, which features entity …

Most existing joint neural models for Information Extraction (IE) use local task-specific
classifiers to predict labels for individual instances (eg, trigger, relation) regardless of their …

With the development of high computational devices, deep neural networks (DNNs), in
recent years, have gained significant popularity in many Artificial Intelligence (AI) …

Named entity recognition and relation extraction are two important fundamental problems.
Joint learning algorithms have been proposed to solve both tasks simultaneously, and many …

Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting the triplets of target
entities, their associated sentiment, and opinion spans explaining the reason for the …

Named entity recognition (NER) task aims at identifying entities from a piece of text that
belong to predefined semantic types such as person, location, organization, etc. The state-of …

In this paper, we propose a new paradigm for the task of entity-relation extraction. We cast
the task as a multi-turn question answering problem, ie, the extraction of entities and …

We introduce a general framework for several information extraction tasks that share span
representations using dynamically constructed span graphs. The graphs are constructed by …