Proper name generation is a seriously understudied phenomenon in automatic text generation. There are potentially many different ways in which a person can be referred to in a text using their name (Barack Hussein Obama II, Barack Obama, Obama, President Obama, etc.) and arguably a text that uses different naming formats in different conditions is more human-like than one that relies on a fixed strategy (e.g., always use the full name).

This project introduces a statistical model able to generate variations of a proper name by taking into account the person to be mentioned, the discourse context and individual variation. The model relies on the REGnames corpus, a dataset with 53,102 proper name references to 1,000 people in different discourse contexts.

The code to build the REGnames corpus is available here.

@InProceedings{castroferreira-wubben-krahmer:2016:INLG,
  author    = {Castro Ferreira, Thiago  and  Wubben, Sander  and  Krahmer, Emiel},
  title     = {Towards proper name generation: a corpus analysis},
  booktitle = {Proceedings of the 9th International Natural Language Generation conference},
  month     = {September 5-8},
  year      = {2016},
  address   = {Edinburgh, UK},
  publisher = {Association for Computational Linguistics},
  pages     = {222--226},
  url       = {http://anthology.aclweb.org/W16-6636}
}

The script performed to obtain the results described at the EACL 2017 paper can be found here

The models described at the paper are available here

Creators: Thiago Castro Ferreira, Emiel Krahmer and Sander Wubben

Tilburg center for Cognition and Communication Department - Tilburg University