@inproceedings{schwarzenberg-etal-2019-neural,
title = "Neural Vector Conceptualization for Word Vector Space Interpretation",
author = "Schwarzenberg, Robert and
Raithel, Lisa and
Harbecke, David",
booktitle = "Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}",
month = jun,
year = "2019",
address = "Minneapolis, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-2001",
doi = "10.18653/v1/W19-2001",
pages = "1--7",
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.",
}
Markdown (Informal)
[Neural Vector Conceptualization for Word Vector Space Interpretation](https://aclanthology.org/W19-2001) (Schwarzenberg et al., RepEval 2019)
ACL