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Neural Vector Conceptualization for Word Vector Space Interpretation
lib:3f6c3a2991475490 (v1.0.0)
Authors: Robert Schwarzenberg,Lisa Raithel,David Harbecke
Where published: WS 2019 6
ArXiv: 1904.01500
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1904.01500v1
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 relations between word vectors and concepts. Furthermore, we show that it produces considerably less entropic concept activation profiles than the popular cosine similarity.
Where published: WS 2019 6
ArXiv: 1904.01500
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1904.01500v1
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 relations between word vectors and concepts. Furthermore, we show that it produces considerably less entropic concept activation profiles than the popular cosine similarity.
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