With the advent of Web 2.0, there exist many online platforms that result in massive textual-
data production. With ever-increasing textual data at hand, it is of immense importance to …

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 …

In a surprising turn, Large Language Models (LLMs) together with a growing arsenal of
prompt-based heuristics now offer powerful off-the-shelf approaches providing few-shot …

Bias in textual data can lead to skewed interpretations and outcomes when the data is used.
These biases could perpetuate stereotypes, discrimination, or other forms of unfair …

Natural language processing (NLP) is crucial in the current processing of data because it
takes into account many sources, formats, and purposes of data as well as information from …

With the growing number of electronic health record data, clinical NLP tasks have be-come
increasingly relevant to unlock valu-able information from unstructured clinical text. Although …

As great amounts of food-related information is presented in the form of heterogeneous
textual data, computer-based methods are useful to automatically extract such information …

Named entity recognition (NER) is a key component of many scientific literature mining
tasks, such as information retrieval, information extraction, and question answering; …

Food composition data is important for stakeholders and users active in the areas of food,
nutrition and health. New challenges related to the quality of food composition data reflect …

In this work, we propose a novel weighted distributional semantic model for unsupervised
Named Entity Recognition (NER) in domain specific texts, specifically focusing on …