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Conditional random fields (Lafferty et al., 2001) are quite effective at sequence labeling tasks like shal- low parsing (Sha and Pereira, 2003) and named- entity extraction (McCallum and Li, 2003). …...

We first show how a structural locality bias can improve the accuracy of state-of-the-art dependency grammar induction models trained by EM from unannotated examples (Klein and Manning, 2004). Next, by …...

AbstractMost state-of-the-art systems today produce morphological analysis based only on orthographic patterns. In contrast, we propose a model for unsupervised morphological analysis that integrates orthographic and semantic views of words. …...

AbstractLearning low-dimensional representations for entities and relations in knowledge graphs using contrastive estimation represents a scalable and effective method for inferring connectivity patterns. A crucial aspect of contrastive learning approaches …...

AbstractWhen training most modern reading comprehension models, all the questions associated with a context are treated as being independent from each other. However, closely related questions and their corresponding answers …...