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
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 2839–2848
- Language:
- URL:
- https://aclanthology.org/C16-1267
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/C16-1267.pdf