The paper presents an iterative bidirectional clustering of adjectives and nouns based on a cooccurrence 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.
Most recently, REF use a clustering approach based on adjective-noun co-occurrences in order to induce clusters of German adjectives that constitute the value space of an attribute.
Exploring the value space of attributes: Unsupervised bidirectional clustering of adjectives in German
COLING
Computer Science