Ben Medlock


Fixing paper assignments

  1. Please select all papers that belong to the same person.
  2. Indicate below which author they should be assigned to.
Provide a valid ORCID iD here. This will be used to match future papers to this author.
Provide the name of the school or the university where the author has received or will receive their highest degree (e.g., Ph.D. institution for researchers, or current affiliation for students). This will be used to form the new author page ID, if needed.

TODO: "submit" and "cancel" buttons here


2011

pdf bib
A New Dataset and Method for Automatically Grading ESOL Texts
Helen Yannakoudakis | Ted Briscoe | Ben Medlock
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2007

pdf bib
Weakly Supervised Learning for Hedge Classification in Scientific Literature
Ben Medlock | Ted Briscoe
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

2006

pdf bib
An Introduction to NLP-based Textual Anonymisation
Ben Medlock
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

We introduce the problem of automatic textual anonymisation and present a new publicly-available, pseudonymised benchmark corpus of personal email text for the task, dubbed ITAC (Informal Text Anonymisation Corpus). We discuss the method by which the corpus was constructed, and consider some important issues related to the evaluation of textual anonymisation systems. We also present some initial baseline results on the new corpus using a state of the art HMM-based tagger. We introduce the problem of automatic textual anonymisation and present a new publicly-available, pseudonymised benchmark corpus of personal email text for the task, dubbed ITAC (Informal Text Anonymisation Corpus). We discuss the method by which the corpus was constructed, and consider some important issues related to the evaluation of textual anonymisation systems. We also present some initial baseline results on the new corpus using a state of the art HMM-based tagger.