We present an unsupervised method for inducing verb classes from verb uses in gigaword corpora. Our method consists of two clustering steps: verb-specific semantic frames are first induced by clustering verb uses in a corpus and then verb classes are induced by clustering these frames. By taking this step-wise approach, we can not only generate verb classes based on a massive amount of verb uses in a scalable manner, but also deal with verb polysemy, which is bypassed by most of the previous studies on verb clustering. In our experiments, we acquire semantic frames and verb classes from two giga-word corpora, the larger comprising 20 billion words. The effectiveness of our approach is verified through quantitative evaluations based on polysemy-aware gold-standard data.

We present an unsupervised method for inducing semantic frames from verb uses in giga-word corpora. Our semantic frames are verb-specific example-based frames that are distinguished according to their senses. We use the Chinese Restaurant Process to automatically induce these frames from a massive amount of verb instances. In our experiments, we acquire broad-coverage semantic frames from two giga-word corpora, the larger comprising 20 billion words. Our experimental results indicate the effectiveness of our approach.

In this paper, we aim to close the gap from extensive, human-built semantic resources and corpus-driven unsupervised models. The particular resource explored here is VerbNet, whose organizing principle is that semantics and syntax are linked. To capture patterns of usage that can augment knowledge resources like VerbNet, we expand a Dirichlet process mixture model to predict a VerbNet class for each sense of each verb, allowing us to incorporate annotated VerbNet data to guide the clustering process. The resulting clusters align more closely to hand-curated syntactic/semantic groupings than any previous models, and can be adapted to new domains since they require only corpus counts.

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