Nested named entity recognition (NER) has been mainly studied by recognizing inner
entities first, then outer entities, or vice versa, depending on the nesting level. However, the …

PURPOSE A critical task in oncology is extracting information related to cancer metastasis
from electronic health records. Metastasis-related information is crucial for planning …

As the field of Nested Named Entity Recognition (NNER) advances, it is marked by a
growing complexity due to the increasing number of multi-label entity instances. How to …

Objective The impact of social determinants of health (SDoH) on patients' healthcare quality
and the disparity is well known. Many SDoH items are not coded in structured forms in …

Using language models created from large data sources has improved the performance of
several deep learning-based architectures, obtaining state-of-the-art results in several NLP …

The Span-based model can effectively capture the complex entity structure in the text, thus
becoming the mainstream model for nested named entity recognition (Nested NER) tasks …

Despite the high creation cost, annotated corpora are indispensable for robust natural
language processing systems. In the clinical field, in addition to annotating medical entities …

Named entity recognition (NER) is a fundamental task in natural language processing that
aims to identify and classify named entities in text. However, span-based methods for NER …

This paper introduces a new IncidentAI dataset for safety prevention. Different from prior
corpora that usually contain a single task, our dataset comprises three tasks: named entity …

We present our results regarding the automatic construction of a knowledge graph from
historical documents related to the Chilean dictatorship period (1973-1990). Our approach …