A2: Script Knowledge for Modelling Semantic Expectation
Project A2 is concerned with the development of wide-coverage, automatic methods for acquiring script knowledge, thus addressing the absence of such script knowledge bases. Since script event sequences are rarely explicit in natural prose, the project will use crowd-sourcing methods to create suitable corpora for script acquisition. These will then serve as the input for novel script-mining algorithms which will be used to induce psychologically plausible probabilistic script-automata representations.
Finally, distributional models will be applied to determine the semantic similarity of linguistic expressions, as conditioned by script knowledge – methods that are essential for applying scripts to real texts. The script resources created in this project will inform the development of experimental stimuli in A1, and will be used and evaluated directly in the models developed in A3.
Inducing script structure from crowd-sourced event descriptions via semi-supervised clustering. Inproceedings
Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics, Valencia, Spain, 2017.
Calzolari, Nicoletta ; Choukri, Khalid ; Declerck, Thierry ; Grobelnik, Marko ; Maegaard, Bente ; Mariani, Joseph ; Moreno, Asuncion ; Odijk, Jan ; Piperidis, Stelios (Ed.): Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), European Language Resources Association (ELRA), Portorož, Slovenia, 2016, ISBN: 978-2-9517408-9-1.
International Journal of Computer Vision, pp. 1–28, 2015.
Taming the TAME systems. Cahiers Chronos 27, pp. 161–187, Rodopi, Amsterdam/Philadelphia, 2015.
A Case-Study of Automatic Participant Labeling Inproceedings
Proceedings of the International Conference of the German Society for Computational Linguistics and Language Technology (GSCL 2015), 2015.
Word Structure and Word Usage. Proceedings of the NetWordS Final Conference. Pisa, March 30-April 1, 2015, pp. 91–94, Pisa, Italy, 2015.