Exploring the value space of attributes: Unsupervised bidirectional clustering of adjectives in German

Wiebke Petersen, Oliver Hellwig


Abstract
The paper presents an iterative bidirectional clustering of adjectives and nouns based on a co-occurrence matrix. The clustering method combines a Vector Space Models (VSM) and the results of a Latent Dirichlet Allocation (LDA), whose results are merged in each iterative step. The aim is to derive a clustering of German adjectives that reflects latent semantic classes of adjectives, and that can be used to induce frame-based representations of nouns in a later step. We are able to show that the method induces meaningful groups of adjectives, and that it outperforms a baseline k-means algorithm.
Anthology ID:
C16-1267
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
2839–2848
Language:
URL:
https://aclanthology.org/C16-1267
DOI:
Bibkey:
Cite (ACL):
Wiebke Petersen and Oliver Hellwig. 2016. Exploring the value space of attributes: Unsupervised bidirectional clustering of adjectives in German. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2839–2848, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Exploring the value space of attributes: Unsupervised bidirectional clustering of adjectives in German (Petersen & Hellwig, COLING 2016)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp-22-attachments/C16-1267.pdf