Most approaches to extraction multiple relations from a paragraph require multiple passes
over the paragraph. In practice, multiple passes are computationally expensive and this …

As a vital task in natural language processing, relation classification aims to identify relation
types between entities from texts. In this paper, we propose a novel Att-RCNN model to …

This work presents a two-stage deep learning system for Named Entity Recognition (NER)
and Relation Extraction (RE) from medical texts. These tasks are a crucial step to many …

In this paper, we investigate whether it is possible to automatically annotate texts with
ChatGPT or generate both artificial texts and annotations for them. We prepared three …

When we read, our brain processes language and generates cognitive processing data
such as gaze patterns and brain activity. These signals can be recorded while reading …

The rapid growth of electronic health records (EHRs) has led to unprecedented biomedical
data. Clinician access to the latest patient information can improve the quality of healthcare …

Until recently, human behavioral data from reading has mainly been of interest to
researchers to understand human cognition. However, these human language processing …

In this paper, we introduce a new type of shared task—which is collaborative rather than
competitive—designed to support and fosterthe reproduction of research results. We also …

This paper presents a portable toolkit, SwitchNet, for extracting relations from textual input.
We summarize four data protocols for relation extraction tasks, including relation …

Emotion recognition in textual data is a rapidly evolving field with diverse applications. While
the state-of-the-art (SOTA) models based on pre-trained large language models (LLMs) …