2019
DOI: 10.18653/v1/w19-20
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Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for

Abstract: Distributed word vector spaces are considered hard to interpret which hinders the understanding of natural language processing (NLP) models. In this work, we introduce a new method to interpret arbitrary samples from a word vector space. To this end, we train a neural model to conceptualize word vectors, which means that it activates higher order concepts it recognizes in a given vector. Contrary to prior approaches, our model operates in the original vector space and is capable of learning non-linear relation… Show more

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