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.
Sequence to Sequence Learning for Event Prediction Inproceedings
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp. 37-42, Asian Federation of Natural Language Processing, Taipei, Taiwan, 2017.
A Mixture Model for Learning Multi-Sense Word Embeddings Inproceedings
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Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics, pp. 1-11, Association for Computational Linguistics, Valencia, Spain, 2017.
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