Word classes automatically induced from distributional evidence have proved useful many NLP tasks including Named Entity Recognition, parsing and sentence retrieval. The Brown hard clustering algorithm is commonly used in this scenario. Here we propose to use Latent Dirichlet Allocation in order to induce soft, probabilistic word classes. We compare our approach against Brown in terms of efficiency. We also compare the usefulness of the induced Brown and LDA word classes for the semi-supervised learning of three NLP tasks: fine-grained Named Entity Recognition, Morphological Analysis and semantic Relation Classification. We show that using LDA for word class induction scales better with the number of classes than the Brown algorithm and the resulting classes outperform Brown on the three tasks.
Chrupala REF proposed the use of Latent Dirichlet Allocation in order to induce soft, probabilistic word classes and have shown that plugging automatically and efficiently induced LDA word class features to NER task can achieve better results in comparison with Brown clustering while scaling linearly with the number of classes.
Efficient induction of probabilistic word classes with LDA
IJCNLP
Computer Science