8 citations · 18 across the 5 of their papers we have counts for
6 papers
Yes but.. Can ChatGPT Identify Entities in Historical Documents?
Carlos-Emiliano González-Gallardo, Emanuela Boros, Nancy Girdhar +3
Large language models (LLMs) have been leveraged for several years now, obtaining state-of-the-art performance in recognizing entities from modern documents. For the last few month…
DocILE Benchmark for Document Information Localization and Extraction
Štěpán Šimsa, Milan Šulc, Michal Uřičář +8
This paper introduces the DocILE benchmark with the largest dataset of business documents for the tasks of Key Information Localization and Extraction and Line Item Recognition. It…
Archive TimeLine Summarization (ATLS): Conceptual Framework for Timeline Generation over Historical Document Collections
Nicolas Gutehrlé, Antoine Doucet, Adam Jatowt
Archive collections are nowadays mostly available through search engines interfaces, which allow a user to retrieve documents by issuing queries. The study of these collections may…
Contextualizing Emerging Trends in Financial News Articles
Nhu Khoa Nguyen, Thierry Delahaut, Emanuela Boros +2
Identifying and exploring emerging trends in the news is becoming more essential than ever with many changes occurring worldwide due to the global health crises. However, most of t…
The Recent Advances in Automatic Term Extraction: A survey
Hanh Thi Hong Tran, Matej Martinc, Jaya Caporusso +2
Automatic term extraction (ATE) is a Natural Language Processing (NLP) task that eases the effort of manually identifying terms from domain-specific corpora by providing a list of…
Ensembling Transformers for Cross-domain Automatic Term Extraction
Hanh Thi Hong Tran, Matej Martinc, Andraz Pelicon +2
Automatic term extraction plays an essential role in domain language understanding and several natural language processing downstream tasks. In this paper, we propose a comparative…