Software specifications are essential for ensuring the reliability of software systems. Existing
specification extraction approaches, however, suffer from limited generalizability and require …

Abstract Machine learning (ML) systems in natural language processing (NLP) face
significant challenges in generalizing to out-of-distribution (OOD) data, where the test …

Abstract Recently, Few-shot Named Entity Recognition has received wide attention with the
growing need for NER models to learn new classes with minimized annotation costs …

Few-shot Named Entity Recognition (NER) is imperative for entity tagging in limited resource
domains and thus received proper attention in recent years. Existing approaches for few …

Few-shot named entity recognition (NER) exploits limited annotated instances to identify
named mentions. Effectively transferring the internal or external resources thus becomes the …

Few-shot named entity recognition (FS-NER) aims at identifying entities from plain text via a
handful of samples. Many efforts based on prototype learning hardly model the hierarchy in …

Named Entity Recognition (NER) in social media has received much research attention in
the field of natural language processing (NLP) and information extraction. Research on this …

Prompt-based usage of Large Language Models (LLMs) is an increasingly popular way to
tackle many well-known natural language problems. This trend is due, in part, to the appeal …