Starting from the distributional bootstrapping hypothesis, we propose an unsupervised model that selects the most useful distributional information according to its salience in the input, incorporating psycholinguistic evidence. With a supervised Parts-of-Speech tagging experiment, we provide preliminary results suggesting that the distributional contexts extracted by our model yield similar performances as compared to current approaches from the literature, with a gain in psychological plausibility. We also introduce a more principled way to evaluate the effectiveness of distributional contexts in helping learners to group words in syntactic categories.
|Title of host publication||Proceedings of the 6th Workshop on Cognitive Aspects of Computational Language Learning (CogACLL)|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||7|
|Publication status||Published - Sep 2015|