Summary of the paper

Title The Validation of MRCPD Cross-language Expansions on Imageability Ratings
Authors Ting Liu, Kit Cho, Tomek Strzalkowski, Samira Shaikh and Mehrdad Mirzaei
Abstract In this article, we present a method to validate a multi-lingual (English, Spanish, Russian, and Farsi) corpus on imageability ratings automatically expanded from MRCPD (Liu et al., 2014). We employed the corpus (Brysbaert et al., 2014) on concreteness ratings for our English MRCPD+ validation because of lacking human assessed imageability ratings and high correlation between concreteness ratings and imageability ratings (e.g. r = .83). For the same reason, we built a small corpus with human imageability assessment for the other language corpus validation. The results show that the automatically expanded imageability ratings are highly correlated with human assessment in all four languages, which demonstrate our automatic expansion method is valid and robust. We believe these new resources can be of significant interest to the research community, particularly in natural language processing and computational sociolinguistics.
Topics Corpus (Creation, Annotation, etc.), Information Extraction, Information Retrieval, Computer-Assisted Language Learning (CALL)
Full paper The Validation of MRCPD Cross-language Expansions on Imageability Ratings
Bibtex @InProceedings{LIU16.1227,
  author = {Ting Liu and Kit Cho and Tomek Strzalkowski and Samira Shaikh and Mehrdad Mirzaei},
  title = {The Validation of MRCPD Cross-language Expansions on Imageability Ratings},
  booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
  year = {2016},
  month = {may},
  date = {23-28},
  location = {Portoro┼ż, Slovenia},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  address = {Paris, France},
  isbn = {978-2-9517408-9-1},
  language = {english}
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