View Seminar Video Abstract Transition-based dependency parsers are fast, surprisingly accurate and easy to implement. However, many formal aspects of these parsing systems are not well understood. Specifically, little can be said about the effect[…]
View Seminar Video Abstract Audio source separation is an extremely useful process but most of the time not a goal by itself. Even though most research focuses on better separation quality, ultimately separation is needed[…]
View Seminar Video Abstract I am motivated by structured prediction problems in NLP and social network analysis. Markov Random Fields (MRFs) and other Probabilistic Graphical Models (PGMs) are suitable for representing structured prediction: they can[…]
View Seminar Video Abstract Semantic information embedded in the speech signal manifests itself in a dynamic process rooted in the deep linguistic hierarchy as an intrinsic part of the human cognitive system. Modeling both the[…]
View Seminar Video Abstract The transition-based approach to dependency parsing has become popular thanks to its simplicity and efficiency. Systems like MaltParser achieve linear-time parsing with projective dependency trees using locally trained classifiers to predict[…]
View Seminar Video Abstract A typical problem in speech and language processing has a very large number of training examples, is sequential, highly structured, and has a unique measure of performance, such as the word[…]
View Seminar Video Abstract Computational approaches to problems in Natural Language Understanding and Information Access and Extraction often involve assigning values to sets of interdependent variables. Examples of tasks of interest include semantic role labeling[…]
