%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 }