Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks with different data modalities. A PFM (eg, BERT, ChatGPT, and GPT-4) is …

The development of digital technology promotes the construction of the Intangible cultural
heritage (ICH) database but the data is still unorganized and not linked well, which makes …

In this paper, we present a Linguistic Informed Multi-Task BERT (LIMIT-BERT) for learning
language representations across multiple linguistic tasks by Multi-Task Learning (MTL) …

Multi-disciplinary and inter-disciplinary collaboration can be an appropriate response to
tackling the increasingly complex problems faced by today's society. Scientific disciplines …

Both syntactic and semantic structures are key linguistic contextual clues, in which parsing
the latter has been well shown beneficial from parsing the former. However, few works ever …

In this study, we propose a method designed to extract named entities and relations from
unstructured text based on table representations. To extract named entities, the proposed …

Real-world named entity recognition (NER) datasets are notorious for their noisy nature,
attributed to annotation errors, inconsistencies, and subjective interpretations. Such noises …

Abstract Video Question Answering (VideoQA) concerns the realization of models able to
analyze a video, and produce a meaningful answer to visual content-related questions. To …

Transformer-based language models trained on large natural language corpora have been
very useful in downstream entity extraction tasks. However, they often result in poor …

This paper describes the Integration of Archives and Cultural Places (IaCuP) project, which
aims to integrate information about a historical cemetery, including its map and grave …