5 accepted papers
We present SynCABEL (Synthetic Contextualized Augmentation for Biomedical Entity Linking), a framework that addresses a central bottleneck in supervised biomedical entity linking (BEL): the scarcity of expert-annotated training data. SynCABEL leverages large language models to generate context-rich
Background: Transformer-based language models have shown strong performance on many Natural Language Processing (NLP) tasks. Masked Language Models (MLMs) attract sustained interest because they can be adapted to different languages and sub-domains through training or fine-tuning on specific corpora…
Large language models (LLMs) have become the preferred solution for many natural language processing tasks. In low-resource environments such as specialized domains, their few-shot capabilities are expected to deliver high performance. Named Entity Recognition (NER) is a critical task in information…
We explore the task of event extraction and classification by harnessing the power of distant supervision. We present a novel text labeling method that leverages the redundancy of temporal information in a data lake. This method enables the creation of a large programmatically annotated corpus, allo…
Although recent years have been marked by incredible advances in the whole development process of NLP systems, there are still blind spots in characterizing what is still hampering real-world adoption of models in knowledge-intensive settings. In this paper, we illustrate through a real-world zero-s…