We describe a probabilistic approach to content selection for meeting summarization. We use skipchain Conditional Random Fields (CRF) to model non-local pragmatic dependencies between paired utterances such as QUESTION-ANSWER that typically appear together in summaries, and show that these models outperform linear-chain CRFs and Bayesian models in the task. We also discuss different approaches ...

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Structured prediction models have been found to learn effectively from a few large examples— sometimes even just one. Despite empirical evidence, canonical learning theory cannot guarantee generalization in this setting because the error bounds decrease as a function of the number of examples. We therefore propose new PAC-Bayesian generalization bounds for structured prediction that decrease as...

In this paper, we propose an automatic sentence classification model that can map sentences of a given biomedical abstract into set of pre-defined categories which are used for Evidence Based Medicine (EBM). In our model we explored the use of various lexical, structural and sequential features and worked with Conditional Random Fields (CRF) for classification. Results obtained with our propose...

Urban land cover is one of the fastest global growing land cover types which permanently alters land surface properties and atmospheric interactions, often initiating an urban heat island effect. Urbanisation comprises a number of land cover changes within metropolitan regions. However, these complexities have been somewhat neglected in temperature analysis studies of the urban heat island effe...

We consider the task of generating transliterated word forms. To allow for a wide range of interacting features, we use a conditional random field (CRF) sequence labeling model. We then present two innovations: a training objective that optimizes toward any of a set of possible correct labels (since more than one transliteration is often possible for a particular input), and a k-best reranking ...

Determining the semantic intent of web queries not only involves identifying their semantic class, which is a primary focus of previous works, but also understanding their semantic structure. In this work, we formally define the semantic structure of noun phrase queries as comprised of intent heads and intent modifiers. We present methods that automatically identify these constituents as well a...

This paper presents a sequential labeling approach for tracking the dialog states for the cases of goal changes in a dialog session. The tracking models are trained using linear-chain conditional random fields with the features obtained from the results of SLU. The experimental results show that our proposed approach can improve the performances of the sub-tasks of the second dialog state track...