  Clustering a lexicon of words is a well-studied problem in natural language
processing (NLP). Word clusters are used to deal with sparse data in
statistical language processing, as well as features for solving various NLP
tasks (text categorization, question answering, named entity recognition and
others).
  Spectral clustering is a widely used technique in the field of image
processing and speech recognition. However, it has scarcely been explored in
the context of NLP; specifically, the method used in this (Meila and Shi, 2001)
has never been used to cluster a general word lexicon.
  We apply spectral clustering to a lexicon of words, evaluating the resulting
clusters by using them as features for solving two classical NLP tasks:
semantic role labeling and dependency parsing. We compare performance with
Brown clustering, a widely-used technique for word clustering, as well as with
other clustering methods. We show that spectral clusters produce similar
results to Brown clusters, and outperform other clustering methods. In
addition, we quantify the overlap between spectral and Brown clusters, showing
that each model captures some information which is uncaptured by the other.
