This paper proposes a data-driven method for concept-to-text generation, the task of automatically producing textual output from non-linguistic input. A key insight in our approac...
We compare and contrast two different models for detecting sentence-like units in continuous speech. The first approach uses hidden Markov sequence models based on N-grams and max...
Yang Liu, Andreas Stolcke, Elizabeth Shriberg, Mar...
We use a generative history-based model to predict the most likely derivation of a dependency parse. Our probabilistic model is based on Incremental Sigmoid Belief Networks, a rec...
This paper presents a probabilistic grammar approach to the recognition of complex events in videos. Firstly, based on the original motion features, a rule induction algorithm is a...
In this paper, we present an algorithm for learning a generative model of natural language sentences together with their formal meaning representations with hierarchical structure...
Wei Lu, Hwee Tou Ng, Wee Sun Lee, Luke S. Zettlemo...