The biomedical literature is growing rapidly, and the extraction of meaningful information
from the large amount of literature is increasingly important. Biomedical named entity …

NLP has achieved great progress in the past decade through the use of neural models and
large labeled datasets. The dependence on abundant data prevents NLP models from being …

Event extraction (EE) is a crucial research task for promptly apprehending event information
from massive textual data. With the rapid development of deep learning, EE based on deep …

Recent years have seen the paradigm shift of Named Entity Recognition (NER) systems
from sequence labeling to span prediction. Despite its preliminary effectiveness, the span …

The ability to generalise well is one of the primary desiderata of natural language
processing (NLP). Yet, what'good generalisation'entails and how it should be evaluated is …

Past progress on neural models has proven that named entity recognition is no longer a
problem if we have enough labeled data. However, collecting enough data and annotating …

Joint entity and relation extraction framework constructs a unified model to perform entity
recognition and relation extraction simultaneously, which can exploit the dependency …

With the proliferation of models for natural language processing tasks, it is even harder to
understand the differences between models and their relative merits. Simply looking at …

Named Entity Recognition (NER) is one of the first stages in deep language understanding
yet current NER models heavily rely on human-annotated data. In this work, to alleviate the …

The cybersecurity report provides unstructured actionable cyber threat intelligence (CTI) with
detailed threat attack procedures and indicators of compromise (IOCs), eg, malware hash or …