Abstract
Word embeddings are high-dimensional vector representations of words and are thus difficult to interpret. In order to deal with this, we introduce an unsupervised parameter free method for creating a hierarchical graphical clustering of the full ensemble of word vectors and show that this structure is a geometrically meaningful representation of the original relations between the words. This newly obtained representation can be used for better understanding and thus improving the embedding algorithm and exhibits semantic meaning, so it can also be utilized in a variety of language processing tasks like categorization or measuring similarity.- Anthology ID:
- W17-2404
- Volume:
- Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Martin Riedl, Swapna Somasundaran, Goran Glavaš, Eduard Hovy
- Venue:
- TextGraphs
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 30–38
- Language:
- URL:
- https://aclanthology.org/W17-2404
- DOI:
- 10.18653/v1/W17-2404
- Cite (ACL):
- Thomas Alexander Trost and Dietrich Klakow. 2017. Parameter Free Hierarchical Graph-Based Clustering for Analyzing Continuous Word Embeddings. In Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing, pages 30–38, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Parameter Free Hierarchical Graph-Based Clustering for Analyzing Continuous Word Embeddings (Trost & Klakow, TextGraphs 2017)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/W17-2404.pdf