Summary of the paper
| Title | Towards Shared Datasets for Normalization Research |
|---|---|
| Authors | Orphee De Clercq, Sarah Schulz, Bart Desmet and Veronique Hoste |
| Abstract | In this paper we present a Dutch and English dataset that can serve as a gold standard for evaluating text normalization approaches. With the combination of text messages, message board posts and tweets, these datasets represent a variety of user generated content. All data was manually normalized to their standard form using newly-developed guidelines. We perform automatic lexical normalization experiments on these datasets using statistical machine translation techniques. We focus on both the word and character level and find that we can improve the BLEU score with ca. 20% for both languages. In order for this user generated content data to be released publicly to the research community some issues first need to be resolved. These are discussed in closer detail by focussing on the current legislation and by investigating previous similar data collection projects. With this discussion we hope to shed some light on various difficulties researchers are facing when trying to share social media data. |
| Topics | Social Media Processing, Other |
Full paper  |
Towards Shared Datasets for Normalization Research |
| Bibtex | @InProceedings{DECLERCQ14.733, author = {Orphee De Clercq and Sarah Schulz and Bart Desmet and Veronique Hoste}, title = {Towards Shared Datasets for Normalization Research}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |