Summary of the paper
| Title | Getting Reliable Annotations for Sarcasm in Online Dialogues |
|---|---|
| Authors | Reid Swanson, Stephanie Lukin, Luke Eisenberg, Thomas Corcoran and Marilyn Walker |
| Abstract | The language used in online forums differs in many ways from that of traditional language resources such as news. One difference is the use and frequency of nonliteral, subjective dialogue acts such as sarcasm. Whether the aim is to develop a theory of sarcasm in dialogue, or engineer automatic methods for reliably detecting sarcasm, a major challenge is simply the difficulty of getting enough reliably labelled examples. In this paper we describe our work on methods for achieving highly reliable sarcasm annotations from untrained annotators on Mechanical Turk. We explore the use of a number of common statistical reliability measures, such as Kappa, Karger's, Majority Class, and EM. We show that more sophisticated measures do not appear to yield better results for our data than simple measures such as assuming that the correct label is the one that a majority of Turkers apply. |
| Topics | Social Media Processing, Opinion Mining / Sentiment Analysis |
Full paper  |
Getting Reliable Annotations for Sarcasm in Online Dialogues |
| Bibtex | @InProceedings{SWANSON14.1063, author = {Reid Swanson and Stephanie Lukin and Luke Eisenberg and Thomas Corcoran and Marilyn Walker}, title = {Getting Reliable Annotations for Sarcasm in Online Dialogues}, 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} } |