This repository contains a ground truth corpus for semantic frame disambiguation, acquired with crowdsourcing and processed with CrowdTruth metrics that capture ambiguity in annotations by measuring inter-annotator disagreement. The dataset contains annotations for 433 sentence-word pairs from the FrameNet corpus v.1.7, with each sentence-word pair annotated for frame disambiguation by 15 workers. The crowdsourced data was collected from Amazon Mechanical Turk. The corpus has been referenced in the following paper: Anca Dumitrache, Lora Aroyo and Chris Welty: Capturing and Interpreting Ambiguity in Crowdsourcing Frame Disambiguation. HCOMP 2018. To replicate the data processing from the paper, use the Jupyter Notebook file CrowdTruth metrics.ipynb. It requires the installation of the CrowdTruth metrics Python package (v >= 2.0). The data aggregated with CrowdTruth metrics is available in folder data/output/ The raw crowdsourcing data is available in folder data/input/ If you find this data useful in your research, please consider citing: @inproceedings{dumitrache2018frames, Author = {Anca Dumitrache and Lora Aroyo and Chris Welty}, Title = {Capturing Ambiguity in Crowdsourcing Frame Disambiguation}, Booktitle = {The sixth AAAI Conference on Human Computation and Crowdsourcing}, Year = {2018} }

{"references": ["Anca Dumitrache, Lora Aroyo and Chris Welty: Capturing and Interpreting Ambiguity in Crowdsourcing Frame Disambiguation. HCOMP 2018. arXiv:1805.00270"]}