We study how different frame annotations complement one another when learning continuous lexical semantics. We learn the representations from a tensorized skip-gram model that consistently encodes syntactic-semantic content better, with multiple 10 gains over baselines.
We present conditional random fields , a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions made in those models. Conditional random fields also avoid a fundamental limitation of maximum entropy Markov models (MEMMs) and other discriminative Markov models based on directed graphical models, which can be biased towards states with few successor states. We present iterative parameter estimation algorithms for conditional random fields and compare the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data. This research describes SemLink, a comprehensive resource for Natural Language Processing that maps and unifies several highquality lexical resources: PropBank, VerbNet, FrameNet, and the recently added OntoNotes sense groupings. Each of these resources was created for slightly different purposes, and therefore each carries unique strengths and limitations. SemLink allows users to leverage the strengths of each resource and provides the groundwork for incorporating these lexical resources effectively into linked data resources. SemLink and the resources included therein are discussed with a focus on the value of using lexical resources in a complementary fashion. Recent improvements to SemLink, including the addition of a new resource, the OntoNotes sense groupings, are described. Work to address future goals, including further expansion of SemLink, is also discussed.