相关论文: A Semantically Enriched Dataset based on Biomedica…
Named entity recognition (NER) is the task to identify mentions of rigid designators from text belonging to predefined semantic types such as person, location, organization etc. NER always serves as the foundation for many natural language…
Being able to train Named Entity Recognition (NER) models for emerging topics is crucial for many real-world applications especially in the medical domain where new topics are continuously evolving out of the scope of existing models and…
Background: The COVID-19 pandemic has caused severe impacts on health systems worldwide. Its critical nature and the increased interest of individuals and organizations to develop countermeasures to the problem has led to a surge of new…
Objective: This study aims to consider small graphs of concepts and exploit them for expressing graph searches over existing COVID-19-related literature, leveraging the increasing use of graphs to represent and query scientific knowledge…
Timely access to accurate scientific literature in the battle with the ongoing COVID-19 pandemic is critical. This unprecedented public health risk has motivated research towards understanding the disease in general, identifying drugs to…
The COVID-19 global pandemic has resulted in international efforts to understand, track, and mitigate the disease, yielding a significant corpus of COVID-19 and SARS-CoV-2-related publications across scientific disciplines. As of May 2020,…
The task of Named Entity Recognition (NER) is an important component of many natural language processing systems, such as relation extraction and knowledge graph construction. In this work, we present a simple and effective approach for…
We present the Neural Covidex, a search engine that exploits the latest neural ranking architectures to provide information access to the COVID-19 Open Research Dataset curated by the Allen Institute for AI. This web application exists as…
Information resources such as newspapers have produced unstructured text data in various languages related to the corona outbreak since December 2019. Analyzing these unstructured texts is time-consuming without representing them in a…
Since late 2019, COVID-19 has quickly emerged as the newest biomedical domain, resulting in a surge of new information. As with other emergent domains, the discussion surrounding the topic has been rapidly changing, leading to the spread of…
The COVID-19 pandemic triggered a wave of novel scientific literature that is impossible to inspect and study in a reasonable time frame manually. Current machine learning methods offer to project such body of literature into the vector…
Information extraction techniques, including named entity recognition (NER) and relation extraction (RE), are crucial in many domains to support making sense of vast amounts of unstructured text data by identifying and connecting relevant…
In 2020, the White House released the, "Call to Action to the Tech Community on New Machine Readable COVID-19 Dataset," wherein artificial intelligence experts are asked to collect data and develop text mining techniques that can help the…
While the COVID-19 pandemic continues its global devastation, numerous accompanying challenges emerge. One important challenge we face is to efficiently and effectively use recently gathered data and find computational tools to combat the…
Artificial intelligence (AI) has become indispensable for managing and processing the vast amounts of data generated during the COVID-19 pandemic. Ontology, which formalizes knowledge within a domain using standardized vocabularies and…
Named Entity Recognition (NER) is a core natural language processing task in which pre-trained language models have shown remarkable performance. However, standard benchmarks like CoNLL 2003 do not address many of the challenges that…
This study is dedicated to exploring the application of prompt learning methods to advance Named Entity Recognition (NER) within the medical domain. In recent years, the emergence of large-scale models has driven significant progress in NER…
Increasing number of COVID-19 research literatures cause new challenges in effective literature screening and COVID-19 domain knowledge aware Information Retrieval. To tackle the challenges, we demonstrate two tasks along withsolutions,…
Over the last two decades, the development of the CoNLL-2003 named entity recognition (NER) dataset has helped enhance the capabilities of deep learning and natural language processing (NLP). The finance domain, characterized by its unique…
Background: Finding biomedical named entities is one of the most essential tasks in biomedical text mining. Recently, deep learning-based approaches have been applied to biomedical named entity recognition (BioNER) and showed promising…