Finding Names in Trove: Named Entity Recognition for Australian Historical Newspapers

By Sunghwan Mac Kim, Steve Cassidy. 2015

31 citations recorded by the source corpus.

Subfields: Natural Language Processing, Information Extraction

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Rachel Rudinger
2. Benjamin Van Durme
3. Adam Poliak
4. Aparajita Haldar
5. Yejin Choi
6. Ellie Pavlick
7. Yonatan Belinkov
8. Samuel R. Bowman

In Information Extraction

1. Benjamin Van Durme

Citations in the corpus

• Hypothesis Only Baselines in Natural Language Inference (2018, methodology): “Premise sentences were extracted from Annotated Gigaword (Napoles et al., 2012), image captions (Young et al., 2014), the Internet Argument Corpus (Walker et al., 2012), and fic-tional stories from the GutenTag dataset (Mac Kim and Cassidy, 2015).”
• Entities, Dates, and Languages: Zero-Shot on Historical Texts with T0 (2022, background): “Both NER and the related task of Entity Linking can enhance our ability to search and navigate digitised historical materials (Neudecker et al., 2014; Kim and Cassidy, 2015).”