Neural Vector Conceptualization for Word Vector Space Interpretation
R. Schwarzenberg, L. Raithel, and D. Harbecke. (2019)cite arxiv:1904.01500Comment: NAACL-HLT 2019 Workshop on Evaluating Vector Space Representations for NLP (RepEval).
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 relations between word vectors and
concepts. Furthermore, we show that it produces considerably less entropic
concept activation profiles than the popular cosine similarity.
%0 Journal Article
%1 schwarzenberg2019neural
%A Schwarzenberg, Robert
%A Raithel, Lisa
%A Harbecke, David
%D 2019
%K dnn embedding network neural space vector word
%T Neural Vector Conceptualization for Word Vector Space Interpretation
%U http://arxiv.org/abs/1904.01500
%V abs/1904.01500
%X 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.
@article{schwarzenberg2019neural,
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 relations between word vectors and
concepts. Furthermore, we show that it produces considerably less entropic
concept activation profiles than the popular cosine similarity.},
added-at = {2020-07-23T15:16:56.000+0200},
author = {Schwarzenberg, Robert and Raithel, Lisa and Harbecke, David},
biburl = {https://www.bibsonomy.org/bibtex/2f497841981e5660a437b34a9490f022f/jaeschke},
interhash = {b8d13fead4b264cd8f32f66f50ed90c6},
intrahash = {f497841981e5660a437b34a9490f022f},
keywords = {dnn embedding network neural space vector word},
note = {cite arxiv:1904.01500Comment: NAACL-HLT 2019 Workshop on Evaluating Vector Space Representations for NLP (RepEval)},
timestamp = {2020-07-23T15:16:56.000+0200},
title = {Neural Vector Conceptualization for Word Vector Space Interpretation},
url = {http://arxiv.org/abs/1904.01500},
volume = {abs/1904.01500},
year = 2019
}