Identifying valuable information within the extensive texts documented in natural language
presents a significant challenge in various disciplines. Named Entity Recognition (NER), as …

The automatic semantic structuring of scientific text allows for more efficient reading of
research articles and is an important indexing step for academic search engines. Sequential …

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) …

We propose a comprehensive study of one-stage elicitation techniques for querying a large
pre-trained generative transformer (GPT-3.5-turbo) in the rhetorical role prediction task of …

The legal domain is a vast and complex field that involves a considerable amount of text
analysis, including laws, legal arguments, and legal opinions. Legal practitioners must …

We introduce “pointer-guided segment ordering”(SO), a novel pre-training technique aimed
at enhancing the contextual understanding of paragraph-level text representations in large …

Detecting salient parts in text using natural language processing has been widely used to
mitigate the effects of information overflow. Nevertheless, most of the datasets available for …

Sequential sentence classification (SSC) in scientific publications is crucial for supporting
downstream tasks such as fine-grained information retrieval and extractive summarization …

Manually analyzing large collections of research articles is a time-and resource-intensive
activity, making it difficult to stay on top of the latest research findings. Limitations of …